S E P T 0 9 A U G 1 0

Oxford-Man Institute of Quant i tat ive F inance

A n n U A l R E P o R T

The Oxford-Man Institute would like to acknowledge the extraordinary support of Man Group plc who have generously provided our core funding for the period 2007 - 2015 and more generally for their wider support of the University of Oxford including an endowment for the post of Man Professor of Quantitative Finance.

w E l c o m E

We moved into our long-run home, Eagle House, in August 2009 and

so have enjoyed our first full year here. During this year our faculty and

student body has strengthened. Further, we have welcomed many of

the best researchers from around the world to visit us, give seminars and

attend our conferences.

The core of this year’s report is the articles exploring the work of faculty

members Mike Giles, Jan Obłój and Lan Zhang. As well as these in-depth

interviews, details of the research interests of all of our members can be

found in the subsequent pages.

The report gives special mention to one of our founding members,

Georg Gottlob, who we are delighted to report has recently been made

a Fellow of the Royal Society for his work in computer science.

Details of key Institute events can also be found in this report. A

highlight of the year being the first of an annual OMI conference series

on New Directions and Contemporary Issues in Quantitative Finance

organised by Thaleia Zariphopoulou and Xunyu Zhou. The idea of the

series is to focus each year on three topics, bringing together the maths

and economics perspectives of the research problem. This year’s topics

were Information Percolation in Financial Markets, Financial Bubbles,

and Principal-Agent Problem and Contract Theory.

An insert is also included providing an insight into life at the Institute

from the perspectives of faculty, associate and student members, visitors

to the Institute and our colleagues from the Man Research Laboratory.

I am also pleased to be able to take this opportunity to report that our

main research grant from Man Group, which provides the core funding

for the Institute, has been extended so that it now runs until 2015. We

are extremely grateful to them for their continued support.

Finally I would like to extend my thanks to Thaleia Zariphopoulou who

has edited this year’s report.

Professor Neil Shephard, FBA

Director of the oxford-man Institute

August 2010

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I am pleased to introduce this, the third Annual Report of the Oxford-Man Institute, in a year that has marked more milestones in our development.

on the 24th September 2009, the oxford-man Institute celebrated the official opening of its new home, Eagle House, by lord Patten of Barnes, chancellor of the University and Peter clarke, chief Executive of man Group plc

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0 3G E o R G G o T T l o B

Georg Gottlob, Professor of Computing Science at the University of Oxford and senior member of the Oxford-Man Institute, was earlier this year elected as a Fellow of the Royal Society.

The decision recognises his contributions to fields including artificial

intelligence, database theory and the logical representation of knowledge.

“It’s a big honour for me personally. I knew I’d been

nominated, but I didn’t really expect to be chosen.

Going up during the ceremony and signing my name in

the book of members that also includes scientists like

Newton and Darwin was a great moment,” he says,

though he adds that the quill pen new fellows have to

use didn’t make the task easy.

Much of Gottlob’s recent research focuses on automatically extracting

information from the internet. Most websites are designed to be read

by humans; while we can easily recognise which onscreen symbols

represent an item’s price, computers find the task impossible.

He has developed software that lets users show a computer where to

find specific information on a particular website. The computer can

then regularly visit that site and report what it finds. Gottlob co-

founded Lixto, a Vienna-based company offering the technology to

corporate clients for tasks like monitoring rivals’ prices.

He now leads the Diadem project, which aims to take the concept

much further by creating software to extract information without

human guidance from websites within a particular domain, pulling out

highly-structured data that can then be manipulated and analysed in

other applications.

The five-year project received a €2.4m European Research Council

Advanced Investigators Award in 2009, with additional pledged

funding from the James Martin 21st Century School. Gottlob’s newly-

assembled team has already made progress towards creating a new

programming language suited to data extraction and manipulation,

and work on the project is now moving into high gear.

It could transform how we use the internet, tapping into

the ‘deep web’ – the information that’s held in databases

behind individual websites, beyond the reach of today’s

search engines. For companies, the technology could be

still more important, shedding unprecedented light on

market conditions and competitors’ activities. Search

giants like Google, Yahoo and Microsoft have already

shown interest.

There could be important financial applications too. Indeed markets

move on economic data like inflation figures or housing market

announcements. These numbers are calculated from large numbers of

prices that are already available, albeit scattered around the internet. By

monitoring prices on retail websites, for example, a hedge fund could

potentially anticipate official inflation data and position itself accordingly.

Another current interest with possible business applications is

combinatorial auctions. In these, participants bid on whole packages

of goods at a time rather than on single items. They could be useful in

many situations, but deciding who has won the auction is challenging.

A computer must search through a vast number of possible solutions

before deciding which is most profitable for the vendor. In some cases,

it may never find an answer.

Gottlob has worked extensively on this kind of problem, known as ‘NP-

complete’. His talk at the Royal Society New Fellows Seminar discussed

the possibility of identifying and isolating the limited number of

difficult cases in which NP-complete problems are insoluble, in order to

concentrate on the majority of much easier cases. If such an approach

could make it easier to determine a combinatorial auction’s winner, the

method could become useful in many business-to-business contexts – one

obvious application would be auctioning airport landing slots to airlines.

A convivial type, Gottlob is also enthusiastic about the

social side of membership. “I’ve become a member of

the Royal Society Club, and I’m really looking forward

to joining them for interesting conversation and good

company over meals. The club includes no more than 100

current members, so I’m grateful to Terry Lyons [Wallis

Professor of Mathematics, Royal Society Fellow and OMI

member] for nominating me to join it!”

oxford-man Institute member becomes Fellow of the Royal Society

Mike Giles’ mathematical interests have taken him from helping design better jet engines into everything from high-performance computing to valuing complex derivatives portfolios and working with others to process vast amounts of astronomical data.

Now Professor of Scientific Computing in the Mathematical Institute

and a member of OMI, Giles spent the first part of his working life as an

aeronautical engineer specialising in computational fluid dynamics (CFD)

– the mathematical study of how fluid moves around obstacles.

As a young mathematician at Cambridge, he spent his summers working

at Rolls-Royce in Derby. The relationship was to last decades, continuing

through graduate study and seven years as a professor at MIT and into a

long spell researching CFD at Oxford. Giles only moved into finance in the

last few years, but it hasn’t taken him long to make an impression.

CFD helps design everything from cars to wind turbines, and is crucial

to the aeronautics industry’s efforts to make jet engines more efficient.

Once, engineers would painstakingly build and test multiple new designs;

now they use computers and the mathematical techniques developed by

researchers like Giles to simulate their behaviour. This gives them far more

insight into their designs’ aerodynamics, and is responsible for much of the

last few decades’ huge advance in aircraft efficiency.

In the middle of a successful career, Giles decided to decamp and see what

the mathematical and computational tool-kit he’d built up could do in

other areas. Embarking on a radical new direction at this stage might look

daring, but he says a new challenge was overdue.

“The ratio of perspiration to inspiration was getting

unacceptable,” he grins. “CFD modelling programs now

stretch over tens of thousands of lines of code; working

in the field was getting more and more like managing

a large software project. I needed a change, and one of

the real joys of academic life is that you can do that.”

Giles settled on quantitative finance as his new field, partly because he

knew certain techniques were similar to the CFD equations he was used

to manipulating. But his biggest achievement so far has come in an area

he’d started with no plans to work in – using Monte Carlo methods to

help banks and other institutions quantify risk more efficiently. It’s a great

example of the benefits of cross-fertilisation between different fields.

Intending to brush up on less familiar areas of financial maths so he could

teach them, Giles took a class in London on the theory of Monte Carlo

simulation. This is a way of understanding how a complex situation may

develop by simulating it thousands of times with different random inputs

to account for the uncertainty of future events.

Investment banks do this to model risks involving

multiple sources of uncertainty, like how moves in

multiple markets would affect a derivatives portfolio. It

works well, but takes huge computing power – banks

need whole rooms of expensive, electricity-hungry

computers just for these simulations.

The course was on Monte Carlo methods for pricing option portfolios,

taught by Professor Paul Glasserman of Columbia Business School. During

a break, Giles asked how much the subject was being simplified for the

students’ benefit – he’d noticed an area that could be handled much more

efficiently with a branch of mathematics known as ‘adjoint equations’,

used in CFD to calculate how sensitive a complex processes results are to

its inputs. He assumed they were involved behind the scenes, but were

thought too difficult for students at this level.

They weren’t, though. Nobody had ever applied adjoints to Monte

Carlo simulations, so the finance industry was wasting money by buying

much more computer power than it needed. “To someone from a CFD

background, it seemed obvious,” Giles explains. “Luckily for me, people

developing Monte Carlo methods more often come from statistics or

theoretical physics, and nobody had thought of doing this before.”

Giles and Glasserman wrote an article on using adjoints to compute option

portfolios’ sensitivity to interest rate changes. It won them recognition

as 2007’s Quants of the Year from industry journal Risk. Giles knows of

major investment banks that are already putting the theory into practice,

and others are probably following. When trying to calculate a portfolio’s

sensitivity to many factors at once, he thinks his method could reduce the

computational workload by a factor of twenty.

Subsequent work has adapted the method to computing portfolios’

sensitivity to the degree of correlation between different assets, and Giles

thinks more applications remain to be found. Not bad for a first foray into

a new discipline.

from fluid dynamics to finance

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Beyond financial mathematics, another long-standing interest is high-

performance computing, in particular parallel computing – solving big

problems with lots of small, cheap processors rather than relying on a

single very powerful computer. Beyond a certain point the latter approach

becomes impractical, as the laws of physics mean it gets harder and harder

to squeeze more processing power into a given area of silicon while

dispersing the heat it produces. Parallel computing offers a cheaper and

more energy-efficient way forward.

The field was a big part of Giles’ work with Rolls-Royce.

Since moving to OMI he’s kept up the interest, doing

extensive work on tapping the unexploited power of

graphics cards. Graphics processing units (GPUs) were

originally developed to let gamers enjoy ever-smoother

and faster-moving 3D visuals. But in recent years it’s

become clear they’re better for certain heavy-duty

computational tasks than the central processing units

that sit at the heart of most modern computers.

Tasks that lend themselves to parallel processing are particularly

suitable for this approach – GPUs are designed to do many small

things at once, and can be many times faster at it. Until recently,

programming for parallel computing with graphics hardware was

discouragingly hard, but that’s now changing, and researchers are

working out how to link lots of GPUs together to tackle challenges

that would once have seemed impossibly big.

“I’m an evangelist for GPU processing,” Giles says. “You get a lot more

done for your money – at the moment, the ratio is about 10 to 1.” He’s

collaborated with specialists in fields ranging from engineering and

statistics to handling the output of medical imaging hardware in real time.

He’s even starting to work on the Square Kilometre Array project, which

aims to create the biggest radiotelescope ever built by linking receiving

stations which stretch up to 3000km from a central core of dish antennas.

One proposed site stretches across Australia and New Zealand; the other

extends from a base in South Africa to outlying receivers in Ghana, Kenya,

Madagascar and Mauritius. Giles is advising on the high-speed parallel

computers that will be needed to piece together the streams of data

coming from each receiver to form a single detailed image.

Uniting these varied interests is Giles’ search for new ways to apply his

mathematical tools in practical, beneficial ways. The Institute provides a

great venue for this. “I’ve been involved with OMI from the start,” he says.

“It seemed like a natural place for me. Their aim was to bring together

various groups that had been slightly isolated from each other before, and

I’ve always been interested in interdisciplinary work and in the practical

applications of my research.”

“It’s a great environment – you get into very stimulating

discussions with the other people working here.” The

achievements of the past couple

of years bear witness to just how

beneficial those stimulating

conversations can be.

When it comes to financial markets, people tend to focus on how share prices or exchange rates move over a day or longer. But for Lan Zhang, the really interesting bit is what you see only when you look up close.

That means studying not what prices do over weeks, months or years, but

how they behave second by second.

Zhang is a financial statistician and econometrician, and since arriving

from Chicago last year to join OMI as a Senior Research Fellow, as well as

becoming part of the Saïd Business School’s finance group, she’s continued

to work on what’s known as the ‘microstructure’ of financial markets.

Seen at a very small scale, financial market movements

can look meaningless. But the concept of microstructure

suggests that by looking at how prices evolve tick by

tick, order by order, you can gain surprising insights with

much wider application.

Zhang became interested in market microstructure after finishing her

PhD, and has seen the field take off spectacularly over the last few

years. “My interest is in taking financial theory and comparing it to

what the data is telling us,” she explains. “We can’t be satisfied with a

beautiful model; we have to look at the data and ask if it agrees with

what the model is predicting.” Much of her career has been devoted

to this – using the statistical study of high-frequency data to improve

financial modelling. This is even more important now that the financial

crisis has shown the limitations of many widely-used models.

Zhang’s recent work focuses on understanding volatility in financial

markets better. In principle, volatility is the main determinant of

market stability. “The theory says that when looking at data with very

high frequency, the volatility should be observable from transactions or

quotes,” she explains.

So if you looked at the market second-by-second, you

should be able to measure the volatility with perfect

precision. But in reality, the opposite is true: the volatility

appears to explode out of all proportion to market

movements. What’s wrong with the theory?

Zhang and her co-authors found the answer in the theory of what

mathematicians call martingales – these are fair games, situations

in which no player should be at an advantage and there is no free

profit to be made without risk. The central insight was that prices in

financial markets combine information with an element of noise. The

information is the martingale, the sense in which the market is a fair

game, but there is the noise on top.

“In the short run, prices are in theory indistinguishable from a fair

game, but every price has a noisy component,” Zhang elaborates.

“When you look at high-frequency data, the noise starts to swamp the

signal – during a very short period of time, the true price doesn’t move

very much, but the noise does.”

For instance, at the micro level, prices have a tendency to bounce back

and forth between the current bid and ask price as traders buy and sell.

Or when a trader places a block order to sell many shares and there

are not enough buyers to meet the demand, the price repeatedly jerks

downwards as the interest at each price level is exhausted. Neither

of these patterns conveys much information about the real value of

what’s being traded.

By adapting martingale theory to cope with the problem of microstructure

noise, Zhang found a way to modify classical techniques to account for

actual market prices. Estimates of market volatility radically improved, no

matter how small an interval prices were examined over.

“Before our work on this, people knew about the

problem of how noisy second-by-second data was, but

the response was just to use lower-frequency data,”

Zhang explains. “They got less noise, but our work

showed that the volatility estimate they were getting

was still biased, while also imprecise: they’d arbitrarily

thrown out a large amount of data!” Her solution

managed to eliminate the noise altogether while

preserving the usefulness of the complete data.

“This is just one example of how data can guide you to update

theoretical models,” Zhang notes. The solution is already being used

in the markets by major banks and financial institutions, which have

put Zhang et al’s ‘two-scale realised volatility estimator’ (TSRV) to

work measuring market variability. One bank uses the TSRV every five

minutes to get a rolling measure of how volatile the markets are. Not

long ago, this would have been inconceivable.

Zhang extended this work still further to help risk managers by

combining TSRV with the classical GARCH model, to forecast volatility

finding meaning in the microstructure

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in stock indices. The improvements are dramatic – the model’s

predictive power leaps manyfold. She is now working on comparing

the method with other measures of market variability, such as the VIX

volatility index. So far it’s measuring up well.

Another project takes examining high-frequency data to the next stage

by looking at order book information. The book doesn’t just include

the exchange’s best bid and ask prices; it provides far more information

about how much demand there was to buy and sell at a whole range

of prices at each instant. Looking at it literally adds an extra dimension

to each price: in addition to time, there is now also price-space.

Order books can give even more insight into how prices develop, and

Zhang is currently using her statistical skills on this problem. Early results

suggest that individual quotes possess their own martingale + noise

structure. Some of this work is in collaboration with researchers at the

Chicago Mercantile Exchange; she also works with Per Mykland at OMI.

“We’re hoping to peel back the noise and look at the

functions of different bid and ask prices,” Zhang says.

Studying the order book can also help quantify other

hard-to-measure qualities of the market, like liquidity

– how easy it is to buy or sell a given security without

affecting its price.

In fact, order book behaviour provided early warning signs of the loss

of liquidity which was part of the recent financial crisis. Ultimately,

examining this data can provide advance warning of major market

shifts; this could help policy-makers, regulators and exchanges as well

as traders to avoid being caught off guard.

Not many mathematicians have training in sociology, but the value of exposure to new fields was brought home to Jan Obłój early on in his career.

He’s gone from this unusual start to employing probability theory in

search of possible ways to reduce financial markets’ dependence on

models that recent events have shown to be dangerously flawed.

After completing a Master’s in Mathematics in his native Poland,

Obłój did a joint PhD in Probability Theory between Paris VI and the

University of Warsaw. At the same time, he finished his second Master’s

Degree in Sociology at the University of Warsaw.

In France he got into the habit of attending lectures and

seminars in his friends’ areas of expertise. “A lot of the

time I didn’t understand much,” he admits. “But it was

a great way of finding out about new areas – at the end

of the lecture I’d have picked up some of the buzz-words

and had an idea of the kinds of problem people in that

field worked on.”

His interest in learning about new areas remained throughout a

Marie Curie Postdoctoral Fellowship working with Mark Davis at

Imperial College London. And since joining the Oxford-Man Institute

of Quantitative Finance, Obłój has continued to work at the boundary

between mathematics, finance and probability theory.

“I’ve always been interested in real-life applications for my mathematical

work,” he explains. “My doctorate was in pure probability, but I wanted

to study things with concrete applications – so mathematical finance was

a natural area to go into.”

When it was time to move on from Imperial, Oxford was one of only a

few places Obłój considered. “The Institute was central to my decision,”

he says. As he wanted to keep living in London while commuting to

Oxford, he couldn’t take on the teaching and administrative workload

of a college fellow. Instead, he agreed with the Mathematical Institute

that he’d join OMI and be spared both the responsibilities and the

perks of college life. “So OMI is like a college for me. I work here, but

I also come here to eat, meet people and talk about what we’re all

doing,” he adds.

Obłój’s major project in recent years has been the

robust pricing and hedging of derivatives using a branch

of maths that studies what is known as the ‘Skorokhod

embedding problem’.

This involves finding ways to value derivatives that don’t depend on

assuming any particular model. Current methods, by contrast, generally

involve developing a theoretical model of what a derivative’s price should

be relative to the asset that underlies it; calibrating this model using

data from more widely-traded derivatives, then pricing the derivative

in question by assuming that its value shouldn’t leave any possibility of

risk-free profit – a fundamental assumption of financial mathematics. The

Black-Scholes model that the industry uses to value options works this way.

This may work in a normal environment, but the events of the last

couple of years have shown how fragile these model-based ways of

valuing derivatives are. If the model turns out to be wrong, those who

have valued and hedged derivatives using it may face catastrophic losses,

like the ones that contributed to the downfall of high-profile banks.

Finance professionals call this ‘model risk’ – the danger that your model

is exposed by uncooperative reality. By definition we don’t know how

wrong a model could be until the damage has already been done. “It’s

hard to quantify model risk because we don’t really understand it until

it appears,” Obłój explains. “My goal is to develop tools that let us value

and hedge derivatives’ positions without relying on models.”

He’s gone a long way towards achieving this, alongside collaborators

like Alexander Cox at the University of Bath and David Hobson at the

University of Warwick. The new method involves taking information

from prices of actively-traded assets, and then using this to value a less

liquid derivative product using techniques derived from Skorokhod

embeddings, which provide what Obłój describes as a ‘different pair of

glasses to look at the market through’.

The result is a price range within which the derivative in

question’s value must lie. If anyone wants to buy or sell

it outside that range, an arbitrage – free money on offer

for no extra risk – must be available. The new method

doesn’t just make this clear; it can also pinpoint how to

profit from this opportunity.

So far Obłój has successfully developed robust techniques for illiquid

derivatives like double barrier options and weighted variance swaps. In

practice, the appeal of the method lies not in the valuation itself, but

in its use for hedging. If one derivative’s position is threatening to drag

dealing robustly with risk

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down an otherwise sound trading book, robust hedging techniques could

neutralise that position while leaving the rest untouched. They provide

relative security, with a risk profile which is bounded from below.

“If you’ve no idea how to hedge a particular derivative,

my method could be a better choice than a model-based

approach as it at least puts a floor under your losses,”

Obłój explains. “The model hedge will be perfect as long

as the model it’s based on is accurate, but there’s the risk

of a big loss if it fails. A robust hedge is more expensive,

but doesn’t carry this risk.”

Another advantage is that robust hedging involves buying and selling

securities once or twice in order to neutralise the risk in a position,

whereas current model-based techniques entail regularly rebalancing the

hedge to keep it working as conditions change, which may entail large

transaction costs.

Obłój now hopes to take the techniques still further. He thinks working

with econometricians and statisticians could improve them more by

drawing on historical price records rather than just current market prices,

letting him specify the value of the derivatives being priced within a

much tighter range, with, say, 95% confidence.

This kind of interdisciplinary cross-fertilisation is where OMI excels. This

doesn’t just mean academics; also around the building are quantitative

researchers from AHL, part of Man Group, the hedge fund that endowed

the Institute in 2007.

“It’s a very stimulating place to be,” Obłój explains.

“There are people from all kinds of backgrounds to talk

to in the common room, and even if their work doesn’t

have any immediate connection to mine it’s interesting

to hear about topics outside my experience.”

For the moment it’s hard to say how widely the finance industry could

adopt his methods for robust hedging. “I don’t know if it will be taken

up,” Obłój muses. “Before the crisis, I’d have said not. But the events of

the last few years have made people much more aware of model risk. I

believe in some situations these techniques could be very useful.”

is the Lovells Professor of Law and Finance, having previously held posts at the University of Cambridge and the University of Nottingham. He studied law (MA, BCL) at the University of Oxford before completing his LLM at Yale Law School and taking up his first post at the University of Nottingham.

He has held visiting posts at various institutions including Pennsylvania

Law School, the University of Bologna, and Columbia Law School. His

main research interest lies in the integration of legal and economic

analysis, with particular emphasis on the impact of changes in the

law governing insolvency and company law on the real economy. His

law and finance research is principally of an empirical orientation.

He has been involved in policy related projects commissioned by the

Department of Trade and Industry, the Financial Services Authority, and

the Insolvency Service.

joined the Institute in April 2008. She is currently the Rank-Manning Junior Research Fellow in Economics at New College.Karen completed degrees in economics and German at Birmingham University. She came to the University of Oxford to pursue a Master’s Degree in Economics, followed by a DPhil.

Karen’s research interests relate largely to applied microeconomics,

in particular the application of game theory within industrial,

organisational, and public economics. She has pursued research into

several topics connected to finance, recently working on the market

microstructure of financial and betting markets, the comparative

performance of information aggregation mechanisms such as

prediction markets, and theoretical aspects of financial regulation.

Other strands of her work have dealt, from a theoretical perspective,

with the topics of digital piracy, teamwork and leadership, distributed

co-creation, and political extremism.

joined the institute in September 2009 and is a Postdoctoral Research Assistant working in the area of stochastic analysis. His research interests include rough path analysis, interacting particle systems, Malliavin calculus, stochastic differential geometry and Monte Carlo simulation in mathematical finance.

He has a PhD, Undergraduate and Master’s Degrees from the University

of Cambridge.

is Professor of Scientific Computing at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group. He read mathematics at Cambridge before completing a PhD in Aeronautical Engineering at the Massachusetts Institute of Technology (MIT).

He was an Associate Professor at MIT before moving to Oxford in

1992 to join the Computing Laboratory. After working closely with

Rolls-Royce for many years developing computational fluid dynamics

techniques, he moved into the development of Monte Carlo and finite

difference methods in computational finance, which led to his transfer

to the Mathematical Institute in 2008. In 2007 he was named ‘Quant of

the Year’ by Risk magazine, together with Paul Glasserman of Columbia

Business School, for their joint work on the use of adjoints for the

efficient calculation of Monte Carlo sensitivities.

john armour

karen croxson

tom cass

mike giles

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is a Professor of Computing Science. His research interests are database theory, web information processing and theoretical computer science. At the Oxford-Man Institute, he researches data exchange, semantic database and web querying, and automatic web data extraction for betting and quantitative finance.

He was a Professor at the University of Technology, Vienna from 1988-

2005, where he still holds an adjunct appointment. Before that, he was

affiliated with the Italian National Research Council in Genoa, Italy, and

with the Politecnico di Milano, Italy. He has received the Wittgenstein

Award from the Austrian National Science Fund, is a Fellow of the Royal

Society and the Association of Computing Machinery, a member of the

Austrian Academy of Sciences, the German National Academy of Sciences,

and the European Academy of Sciences Academia Europaea in London.

is a Senior Research Fellow at OMI and is affiliated with the Mathematical Institute. Previously a Reader in the Finance Group at Warwick Business School, Vicky held positions at Princeton University, ETH Zurich, and spent six months at the Isaac Newton Institute, University of Cambridge.

Vicky’s research area is mathematical finance with an emphasis on

derivative pricing in incomplete markets, particularly via the utility

indifference approach. She has worked on optimal stopping problems

relating to American option exercise with partial hedging which

have been applied to problems in real and executive stock options.

Recently, Vicky has studied optimal stopping problems under prospect

theory, the results of which help explain disposition effects in financial

markets. Vicky has been involved in major conference organisation for

the Isaac Newton program and the 2010 Quantitative Finance program

at the Fields Institute, Toronto.

is a University Lecturer in Mathematics at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group and the Stochastic Analysis Group. He has a PhD from the University of Cambridge and previously had lectureships in Edinburgh and Bristol. He is Co-editor in Chief of Applied Mathematical Finance.

His research interests in mathematical finance are in the modelling and

pricing of financial derivatives. In particular he has worked on electricity

spot price models and the pricing of complex derivative contracts in

energy markets. He is also interested in credit markets and the pricing

of large portfolio credit baskets contracts. His other research interests

include random walks and diffusion in random and fractal environments,

rough paths, branching processes, random matrices and particle systems.

moved to Oxford in 2004 as a Lecturer within the Department of Statistics. He holds a ‘Programme Leaders’ award in Statistical Genomics from the Medical Research Council. He was awarded the title Professor in 2007 and the Royal Statistical Society’s Guy Medal in Bronze in 2009.

Chris’ research is focussed on Bayesian methods and computation for

high-dimensional inference problems, in particular, analysis techniques

for sequential data structures arising in bioinformatics, statistical

genetics and genetic epidemiology. Within the Oxford-Man Institute

he has ongoing projects with Mike Giles on graphical processing unit

(GPU) implementation of Monte Carlo methods for dynamic inference

problems, and Stephen Roberts on Bayesian nonlinear models. Chris

studied for his PhD in Bayesian Nonlinear Methods within the Statistics

Group in the Department of Mathematics, Imperial College London.

Following this he undertook a postdoc and then lectureship within

the department. In 2002 he was awarded the Royal Statistical Society’s

biennial ‘Research Prize’ for his work in Bayesian statistics.

georg gottlob

vicky henderson

ben hambly

chris holmes

is an applied mathematician working in the Mathematical Institute. He uses applications of differential equations and appropriate approximation procedures. His interests include many aspects of mathematical finance, such as derivatives pricing and models of unusual markets.

is a Postdoctoral Research Fellow at the Department of Economics. He earned a Bachelor’s and Master’s degree from the University of Tokyo, majoring in economics, and he earned a PhD in Economics from the University of Wisconsin-Madison in 2008.

His primary field is financial and time-series econometrics, with an

emphasis on nonparametric testing and estimation problems of

continuous-time economic and financial models. He is currently

interested in the following econometric topics: nonparametric testing

of the stationarity for Markov processes, nonparametric estimation and

forecasting using high frequency data.

is a Postdoctoral Researcher in the Stochastic Analysis Group. His main research interests are in numerical analysis on Wiener space. In particular, he is interested in cubature methods and their application to problems in computational finance and non-linear filtering. He is also working on rough path theory and its probabilistic applications.

completed his PhD in Financial Engineering in 2004 at the Chinese University of Hong Kong. He is a University Lecturer at the Mathematical Institute. His research interests include portfolio selection, behavioural finance, applied stochastic analysis and optimisation.

is a Professor at Carnegie-Mellon University, Pittsburgh, and part time Professor at the University of Oxford. He is a member of the Scientific Board of the Bachelier Finance Society. He currently serves as an associate editor for the academic journals of Stochastic

Processes and their Applications

and Finance and Stochastics.

He was an undergraduate at Moscow Institute of Physics and

Technology and did his graduate studies at Steklov Mathematical

Institute in Moscow. His research primarily focuses on the mathematical

questions of continuous-time finance. He is currently working with

Peter Bank on an equilibrium-based model for price impact effects with

applications to optimal investment, pricing of contingent claims and

optimal execution policy. He is writing an undergraduate textbook on

mathematical finance with William Hrusa (Carnegie-Mellon University)

and is also working on an open source version of C++ library for pricing

of financial derivatives. This library is currently used for the teaching

of Master’s Courses in Computational Finance at Carnegie-Mellon

University and the University of Oxford.

sam howison

shin kanaya

hanqing jin

dmitry kramkov

christian litterer

1 2

m E m B E R S 1 3

is a Lecturer in Finance at the Saïd Business School. He obtained his PhD from Columbia Business School. Before joining the University of Oxford he was a Visiting Researcher at the Institute for Financial Research in Stockholm, Sweden. José specialises in capital markets, investments and investor behaviour.

His research explores the role of information sellers in financial

markets and the use investors make of their financial advice. He is also

interested in the differences exhibited by pension and mutual fund

investors and is currently working on understanding how capable

individuals are of managing their retirement accounts.

is Man Professor of Finance and Statistics. His research currently focuses on high frequency financial data. With Lan Zhang, they have studied inference based on martingale (fair game) theory, often with noise caused by market microstructure.

A recent article shows how to tie this kind of econometrics up with

classical statistics using the statistical device known as contiguity. Mykland

also works on the interface between finance and econometrics, including

how to set intervals for prices when models are uncertain. He believes

high frequency data is the empirical manifestation of continuous-time

financial models, and hopes for the eventual creation of a unified theory

of finance embracing asset pricing, quantitative finance, and econometrics

to help the management of risk in financial markets. Mykland has earlier

worked on fair games and their interface with likelihood theory, and on

survival analysis in medical studies. He says that both of these backgrounds

continue to give him inspiration for the analysis of financial data.

is the Wallis Professor of Mathematics and a Fellow of the Royal Society. Terry is an expert in stochastic analysis; he focuses on developing methodology needed to model interactions between systems that have a high degree of oscillatory behaviour.

For example, developing systematic ways to construct representative

scenarios that identify and quantify outlying behaviour and identify

small but significant risks that systems might be exposed to. He is

working on projects with a number of Oxford academics: With Danyu

Yang he is trying to identify whether a system subject to external

forcing would have large movements (risk of earthquake damage). With

Arend Janssen he is trying to model order books. With Wei Pan he aims

to model and hedge with the volatility surface and is using cubature

methods to simulate solutions to Levy type non-local integral equations.

He is collaborating with Gechun Liang and Zhongmin Qian to extend the

notion of Backward SDE, and with Tom Cass to develop a mathematical

framework for continuum models.

josé martinez terry lyons

per mykland

is a Senior Postdoctoral Research Fellow at OMI. His research interests lie in the field of financial mathematics, specifically the applications of stochastic and functional analysis for the pricing and hedging of financial derivatives.

His current research is concerned with the construction of so-called

‘market models’ – the financial models that are designed to be

permanently consistent with the prices of the liquidly traded derivatives.

In addition, he has done work on static hedging; obtaining exact semi-

static replication strategies for barrier options with European-type

securities in a large class of models. Sergey’s new subject of interest

is portfolio choice, he is working on explicit description of optimal

investment strategies in the presence of untradeable risks, and/or

ambiguity about the investor’s preferences.

is a University Lecturer at the Mathematical Institute. His research in recent years concentrates on rough path analysis, and non-linear partial differential equations arising from applied areas including those from mathematical finance, stochastic analysis, and backward stochastic differential equations.

Zhongmin Qian is also interested in the Ricci curvature and the related

partial differential equations.

is a University Lecturer at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group. Before coming to Oxford he was a Marie Curie Postdoctoral Fellow at Imperial College London.

He holds a PhD in Mathematics from the University Paris IV and

Warsaw University. His general interest is in mathematical finance

and its interplay with probability theory, and he looks at a number of

different problems where tools from martingale theory and stochastic

analysis can be applied. Recent areas of focus include: robust pricing

and hedging of exotic derivatives via the Skorokhod embedding

problem, market completion using options, volatility derivatives and

extrapolation of implied volatility surface, portfolio optimisation under

pathwise constraints and hedge-funds managers’ incentive schemes.

is a Reader in Finance at the Saïd Business School. Tarun has a BA in Mathematics and Economics from Williams College, an MPhil in Economics from Emmanuel College, Cambridge, and a PhD in Business Economics from Harvard University.

He is also a Research Affiliate of the Centre for Economic Policy Research,

London. He has published papers in journals such as the Journal of

Finance, The Journal of Financial Economics and The Review of Financial

Studies. His main areas of interest are capital markets, international

finance and hedge funds. His current research deals with two main

topics: the impact of international investment flows on equities and

foreign currencies in a range of countries; and the performance,

riskiness and capital formation processes of hedge funds. He has taught

courses on international finance, behavioural finance, hedge funds and

investment management for the Master of Financial Economics, MBA,

Executive MBA, and PhD programs at the University of Oxford.

jan obłój sergey nadtochiy

zhongmin qian tarun ramadorai

1 4

is Professor of Information Engineering at the University of Oxford. He studied physics, completed a PhD in Signal Processing and was appointed to the faculty at Imperial College London, before taking up his post in Oxford in 1999. He heads the Pattern Analysis and Machine Learning Research Group.

His main area of research lies in machine learning approaches to data

analysis. He has particular interests in the development of machine

learning theory for problems in time series analysis and decision theory.

Current research applies Bayesian statistics, graphical models and

information theory to diverse problem domains including mathematical

biology, finance and sensor fusion. He has been awarded two medals by

the IEE for papers on Bayesian signal analysis. His current research focuses

on statistical models for sequential change-point analysis, forecasting and

decision making and decentralised multi-agent co-ordination.

is a University Lecturer in the Department of Economics. His research interests focus on financial econometrics. He has carried out work on estimating large dimensional time-varying covariance matrices and has recently focused on the use of high frequency data to more precisely estimate dependence amongst asset returns.

Kevin was an undergraduate at the University of Texas at Austin and did

his PhD at the University of California, San Diego.

is Research Director of the Oxford-Man Institute and Professor of Economics at the University of Oxford. He is a Council Member of the Society of Financial Econometrics and an Associate Editor of Econometrica. Neil is a member of the advisory boards of research centres at Aarhus University and Singapore Management University.

His research interests are mainly focused on econometrics – particularly

working with high frequency data to try and understand financial

volatility and time varying dependence, market microstructure and the

role of jumps in financial markets. He is also interested in the use of

simulation to carry out econometric inference. He was an undergraduate

at York studying economics and statistics. He has carried out graduate

work and taught at LSE. He was elected a Fellow of the Econometric

Society in 2004 and a Fellow of the British Academy in 2006.

is a Research Fellow in Finance and Economics at the Saïd Business School. Ruediger came to the Saïd Business School in 2007 to finish his PhD, which he had previously started at Paderborn University, Germany. Prior to this, he studied business administration and computer science at Paderborn University.

His research interests cover the whole field of private equity, with focus

on the buyout industry. Affiliated areas of interest include leveraged and

structured finance, corporate valuation and mergers and acquisitions.

m E m B E R S 1 5

neil shephard steve roberts

kevin sheppard ruediger stucke

is a University Lecturer at the Mathematical Institute and works on self interacting random processes, especially reinforced random walks, and stochastic algorithms and their relationship with dynamical systems.

His approach is purely mathematical, relating to economics with the study

of reinforcement learning in game theory and to statistical learning.

is the first holder of the Man Professorship of Quantitative Finance and is a member of the Mathematical Institute. Her area of expertise is in financial mathematics, quantitative finance and stochastic optimisation. Her research interests are in portfolio management, investment performance measurement and valuation in incomplete markets.

is a Lecturer in Financial Economics at the Saïd Business School. His research interests include determinants of expected returns, credit risk, mutual funds and portfolio allocation.

is a Senior Postdoctoral Research Fellow and Reader in Financial Econometrics at OMI. Her research focuses on market microstructure, statistical arbitrage, and high frequency financial econometrics. Before joining, she was an Associate Professor at the University of Illinois at Chicago and an Assistant Professor at Carnegie Mellon University (2001-2005).

She was an undergraduate at Peking University and obtained her

Master’s Degree and PhD from the University of Chicago. She spent

2000-2001 at Princeton University as an Exchange Scholar.

pierre tarrès mungo wilson

thaleia zariphopoulou

xunyu zhou

lan zhang

1 6 m E m B E R S

is the Nomura Chair of Mathematical Finance and Director of the Nomura Centre for Mathematical Finance at the University of Oxford. He obtained his PhD at Fudan University in 1989. He currently focuses on the mathematics of behavioural finance.

Prior to joining the University of Oxford he was Chair of Systems

Engineering and Engineering Management at the Chinese University of

Hong Kong. His general research interests are in quantitative finance,

stochastic control and applied probability, while he has recently engaged

in mathematical behavioural finance research. He is a Fellow of IEEE and a

winner of the SIAM Outstanding Paper Prize. He is on the editorial boards

of Mathematical Finance, Operations Research, SIAM Journal on Financial

Mathematics and SIAM Journal on Control and Optimization.

Oxford-Man InstituteA day in the l ife of the. . .

is Professor of Finance at the Saïd Business

School, specialising in the empirical study of

corporate finance, and an associate member

of OMI. He is Director of both the Oxford

Private Equity Institute and Oxford Finance,

the umbrella group for researchers working

in the field at Oxford.

“OMI is the interdisciplinary research centre for finance in

Oxford. It’s one of the University’s unique features – it means

people from different areas see each other a lot more often

than they otherwise would. This is what Oxford Finance was

also set up to encourage, but bringing everyone together

in the same building makes it far easier to turn the vision

of integrating financial research across the whole university

into a reality.

Before OMI, we were all beavering away in our separate

departments and when we did get together it was in a much

more haphazard way. The formation of OMI has created

an environment that allows Oxford Finance to flourish – it

provides both the institutional support and the physical

environment for us to meet in. It doesn’t just enable the

hard research – it also encourages the softer kind of social

interaction that’s critical for progress in any discipline.”

0 2

Many different kinds of people come together at Oxford-Man Institute – professors and students, academics and hedge fund research analysts, mathematicians, statisticians and economists. The next few pages give a variety of perspectives on the Institute as a venue for research, social interaction and academic collaboration.

Tim Jenkinson

0 4

joined OMI in 2007 and is currently finishing

his DPhil in Mathematics. He has now

been offered a position as a Postdoctoral

Researcher at the Institute, which he will

take up later this year. His main research

interest lies in backward stochastic

differential equations and their applications

in finance, but he also has a sideline in

developing models to understand how

bank runs and liquidity crises unfold.

“I’ve been a member of OMI since the beginning

– I think I’ve been here as long as anyone! I’d applied to do

my DPhil at Oxford and I didn’t really know anything about

the Institute. But my supervisors, Terry Lyons [Wallis Professor

of Mathematics] and Zhongmin Qian [University Lecturer in

Mathematics] told me about OMI and helped me apply.

One of my favourite things about working here is the

interdisciplinary co-operation. For example, I did a joint

project with Vicky Henderson [OMI Senior Research Fellow]

on modelling credit risk – I knew about her work before she

joined OMI, but when we met over lunch we found we had

several common interests.

Working with Vicky was really valuable – I got a lot of new

insights from her. There are academics here from many different

areas, but we’re interested in similar problems. Working

together helps us broaden our knowledge and get fresh ideas

– I don’t know of anywhere else that achieves this so well.

Having AHL in the building helps a lot too. We work mainly

with theory but they work in practice, so they think in a

very different way. For example, yesterday I was talking

to Anthony Ledford [Chief Scientist at the Man Research

Laboratory] after a seminar and he made the point that

presentations should be in clear, everyday language with as

few equations as possible. “People come to this kind of event

to get fresh ideas”, he said; “if they want more details they

can always read your paper, but first you have to explain

the basics clearly.” It may sound simple but in an academic

environment it can be easy to lose sight of this – it’s helpful to

have people from a commercial background around to point

this sort of thing out.”

Gechun Liang

0 5

is a University Lecturer in Financial Mathematics

and an associate member of OMI.

“I was invited to join OMI the year after it was created, and

it’s been brilliant for me. It’s a superb set of facilities. I don’t

do my day-to-day work there but I do come along to the

seminars – there are usually around two a week during term

and I’ve co-organised a conference at the Institute. I’d been

through this before and I didn’t much want to do it again,

but I agreed because I knew I’d get very professional support

from the team at the Institute. As it turned out they made the

whole process easy.

I’ve had some very fruitful discussions at the Institute and

I’ve now got some fledgling collaborations that are starting

to get off the ground. For example I’ve been working with

Christoph Reisinger, Sam Howison (both OMI) and Jeremy

Large (AHL) on liquidity models. Nothing very concrete has

come out of this yet, but we’re hoping it will lead to a very

good publication. Another colleague in the maths faculty has

been working with Per Mykland [Man Professor of Finance

and Statistics] after they met at an OMI research seminar and

discovered a common interest.

Research is a mysterious thing. Nobody really understands

why when you put people with different backgrounds

together, things happen.

It’s a process of osmosis – in the long term, bringing

economists, mathematicians, physicists and others together will

definitely improve the work they do. There’s a serious critical

mass of people there now – a good part of our mathematical

finance group now has their offices at Eagle House – and that’s

a function of the resources Man have put into the project.”

Michael Monoy ios

“Early morning is a productive time for me, I’m normally

at my desk by about 7.30am and the first thing I do is to

go to the common room and make myself a cup of coffee.

Gerd Heber, who manages the research computer systems

for OMI, starts at the same time and tends to be the first

person I see – that’s what’s great about our shared space, the

opportunity for our AHL staff and OMI to mix informally in

this purpose-designed environment.

I spend a lot of time liaising with AHL’s various research

groups as research and innovation are key to AHL’s success

and are at the heart of what we do. Quantitative models

lose their efficacy over time unless they are constantly

refreshed. That’s one of the reasons why our research team

has ballooned to 75 people from just 22 five years ago.

The coordination of people and projects is a constant

challenge. Our research team is spread across London,

Oxford and Hong Kong – and we will soon open an office

in Pfaeffikon, Switzerland. This means we use video

conferencing over our own global network for many of our

daily meetings; the technology is so advanced these days

that you can have a conference call with someone in Hong

Kong and feel they are almost in the same room!

Our Research Committee comes up from London on a

regular basis and we spend the entire afternoon in our

meeting room looking at our whole portfolio of initiatives.

Other times I’ll take a more focused look at a single research

theme, trying to pick up on anything that’s behind schedule

or anything that’s going really well. My meeting this

morning looked at proprietary trading proposals. When

we introduce new systems we first trade using Man’s own

money before we introduce those systems into the main

client funds. We have a rich research pipeline at the moment

having invested heavily in research over the last few years.

One of the most important aspects of our collaboration

with OMI is the cross fertilisation of ideas between their

curiosity driven academic research and Man’s commercially

driven research. I liaise with OMI academics in a number of

different ways, including attending regular meetings of their

Research Committee, Management Committee and Executive

Committee. Every three months OMI provides a report on

the quantitative finance research that its researchers are

engaged in. That’s important for us and for the Institute

because it allows the University to get early identification of

any research that could be commercially useful.

But that’s not the only way that I interact with OMI’s academics,

nor is it necessarily the most important. Prof Neil Shephard and

I meet often over lunch or a coffee in the common room. A lot

of thought went into the design of the building, but especially

into the areas we share. It was important to both OMI and Man

that the common room, seminar rooms and lecture theatre were

bright, modern and comfortable, encouraging people to mix

and spend time together there.

Our collaboration with OMI also allows AHL research staff

to attend the regular lectures and seminars held in the

lecture theatre here at Eagle House, where both internal

and prominent external speakers present their work. Once

again this brings both parties together. Additionally our own

researchers have the opportunity to present their work at

the regular Wednesday lunchtime workshops.

I think one of the most rewarding and challenging things

about working for AHL is the variety of people and disciplines

I get to work with and it is even more enjoyable because I get

to do that in the relaxed and collegiate atmosphere alongside

OMI. It’s been a very successful relationship so far and looking

at what has been achieved already I’m really excited about

the prospects for the future.”

0 6

Anthony Ledford is Chief Scientist at AHL and is based in AHL’s Oxford office, the Man Research Laboratory,

which is co-located with OMI. Prior to joining AHL he lectured in statistics at the University

of Surrey. Dr Ledford studied mathematics at Cambridge University and holds a PhD from

Lancaster University in the development and application of multivariate extreme value

methods. In 2001 he joined AHL’s research group and has since then worked on the research

and development of its automated trading and execution models and systems.

0 8

joined the Institute to start a DPhil in

Mathematical Finance with Thaleia

Zariphopoulou, Man Professor of

Quantitative Finance, in October 2009.

“The Institute was a big factor in my decision to come to

Oxford. As an overseas student from outside the EU it’s hard

to get funding to study in the UK, but the Institute has been

extremely supportive. And for doctoral students, this working

space is completely unique in Oxford.

It’s a fantastic environment for research – it’s great to

work alongside so many people from different academic

backgrounds. Many PhD students only meet their supervisor

every couple of weeks, and spend a lot of time in between

working on their own. But I meet people at lunch and

throughout the day, and I’ve already had some incredibly

useful conversations – some people from the AHL laboratory

even helped me come up with the idea for my thesis topic.

It’s particularly helpful to talk to people from a more

commercial background – it definitely adds to the value of

being here. I think this is unique – I know about other places

that emphasise interdisciplinary work among the academic

community, but the commercial element is usually missing,

and this is what makes OMI so special.

I still don’t know if I want to be an academic or join the

financial industry, but being here has certainly exposed me

to both possibilities. Academic researchers in mathematical

finance speak a different language to people from the

financial industry. But I think both can benefit from closer

contact. Academics can get new ideas and correct any

mistakes and unrealistic assumptions; finance professionals

can make their ideas more precise and gain a wider

perspective on their experience.”

Bahman Angoshtari

0 9

is a Quantitative Research Analyst in the

Directional Model Development Team at AHL.

He joined early in 2010 after completing his

DPhil in Economics at the Institute.

“As a doctoral student in economics at OMI it was great to

learn new ways of thinking from researchers in different

fields. I still find it really useful talking to people over lunch,

hearing about what they are doing and picking up new

ideas – sometimes even small things can get you thinking in

a new direction.

For students, the working environment at OMI is amazing

– for example, the IT facilities are brilliant compared to

what’s available elsewhere. I had to run complex computer

simulations for my DPhil; if I’d had to rely on the facilities

at the Economics department, this would have taken much

longer!

I’ve always been interested in the financial markets and

during my doctorate I spent some time getting more

practical experience of them. Initially I had the idea that

working in the finance industry would be rather remote

from doing real research.

Whilst a student at OMI I met the people from AHL, and I

realised you can do high-level research at hedge funds. And

because we were working in the same building, I understood

their working culture and knew it was the kind of place I’d

like to be.

This made the move from being a student to working in

the industry much easier. I’m still in Oxford, still working in

research and I still have the opportunity to go to all OMI

seminars as part of AHL’s collaboration with OMI – it’s been

a very gentle transition. If I hadn’t been exposed to people

from AHL while at OMI, I don’t know if I’d ever have made

the leap into the finance industry.”

Thomas Flury

“I am very impressed by the way the Institute is structured, both

organisationally and physically. It allows for easy interaction and

provides plenty of opportunity for collaboration.

I especially liked the fact that the Man Research Laboratory shares

the common space with academics. This way a theoretician like me

can learn more about the practical aspects of the financial industry.

I hope the arrangement also helps the quants keep abreast of the

latest academic research. I believe both sides can benefit greatly

from such interactions. The commercial outlook usually comes with

a dose of realism we sometimes neglect in the academic world.

Apart from many fruitful exchanges with Professor Zariphopoulou, I

interacted quite a bit with other faculty members too. For example,

Professor Xunyu Zhou and I started a discussion on a possible

research project that would tie a number of emerging ideas on

behavioural science with some classical economic insights.

I also enjoyed immensely the very rich informal seminar schedule – a

wonderful forum for the exchange of ideas. Not only did I get the

chance to learn about the latest research findings of the members

of the Institute; I also had a chance to present my own research.

OMI is quite unique, especially from the point of view of someone

coming from the US. It is very rare there that a for-profit company

has such a clear and healthy long-term vision - embodied in the

investment in cutting-edge academic research - as the Man Group.

I believe that similar collaborations mark the future of any successful

financial enterprise, and I applaud OMI for its vision.”

Gordan Zitkovic is Assistant Professor in the Department of Mathematics at

the University of Texas at Austin. He spent a month at OMI as a long-term visitor in summer

2010, during which he worked with Professor Thaleia Zariphopoulou, a long-standing

collaborator, and taught a short course on the foundations of mathematical finance.

S T U D E n T S 1 7

bahman angoshtariis a first year DPhil student in the Mathematical Institute, University of

Oxford. He holds an MSc in Applied Mathematics from the University

of Twente and a BSc in Industrial Engineering from Sharif University

of Technology, Iran. His research interests lie in the application

of stochastic analysis and control theories in finance, especially in

portfolio choice. He is currently focused on identifying the optimal

investment strategy in a market with co-integrated assets. The results

are directly applicable to pairs-trading, and possible extensions to

statistical arbitrage are under investigation.

youness boutaib is a DPhil student in the Stochastic Analysis Group. Working with Professor

Terry Lyons has drawn his attention to the power of the theory of rough

paths. The theory, along with giving the appropriate frame of solving

equations driven by very irregular signals (like the fractional Brownian

motion), encompasses the previous theories of integration (Stieltjes,

Young and Stratonovitch). He aims to develop a control theory based on

it that would help solve optimisation problems of systems that are ruled

by differential equations driven by rough paths. Applications naturally

include finance and quantum physics and other older classic problems.

christopher fogelberg is a DPhil Student within the Computing Laboratory, University of Oxford.

His research interests are graphical models, particularly their efficient

structural inference in bioinformatics and related fields. He is especially

looking into structural expectation maximisation and dimensional

reduction for dynamic Bayesian networks, and hopes to extend this

research to other graphical models like Markov random fields.

xiaowei gong is a visiting student from the University of Illinois, Chicago. Her research

interests include limit order book modelling, statistics applied to

finance, time series analysis and computational statistics.

arend janssenis a DPhil student in Mathematics at the University of Oxford. He holds

a degree (Diplom in Mathematics) from the University of Freiburg,

Germany. Arend’s research interests lie broadly in Mathematical Finance

and Stochastic Analysis, where he is particularly interested in order book

models. He is also interested in the theory of rough paths and their

applications to finance. Recently, Arend has been working on numerical

solutions of stochastic differential equations driven by rough paths.

lei jinis studying for a DPhil in Mathematics at the University of Oxford under

Ben Hambly. Her research interests include stochastic partial differential

equations, particle systems, optimal stopping problems and credit

derivatives modelling and pricing.

Lei has already submitted her DPhil thesis “Particle Systems and

SPDEs with Application to Credit Modelling”. She will start work with

Goldman Sachs in September 2010.

nathaniel kordais studying for a DPhil in Mathematics at the University of Oxford under

Pierre Tarrès. In 2007 he completed his Undergraduate Master’s Degree

in Mathematics at the University of Oxford. Nathan’s research is focused

on the n-Armed Bandit. An n-Armed Bandit is a simple probabilistic

model of a game in which one repeatedly chooses to play one of n arms,

each of which will yield some reward with a certain fixed, but unknown,

probability. His current interests lie in the asymptotic properties of

various strategies proposed in the literature for this game.

ada lauis studying for a DPhil in Mathematics at the University of Oxford.

Her research interests include wind power forecasting, volatility

forecasting, risk analysis and weather derivatives. Ada is also a research

member of the System Analysis, Modelling and Prediction Group. She

obtained a BSc in Mathematics and Physics at the University of Hong

Kong and an MPhil in Physics at the Chinese University of Hong Kong.

anthony leeis a DPhil student in the Department of Statistics. He completed

Bachelor’s and Master’s Degrees at the University of British Columbia,

specialising in Computer Science.

Anthony’s research interests lie broadly in computational statistics

and Bayesian inference, with emphasis on the design and application

of simulation-based numerical integration techniques in complex,

data-rich domains including those found in quantitative finance. More

specifically, he is interested in enhancing and expanding the use of

advanced Monte Carlo methods, such as Markov chain Monte Carlo

and sequential Monte Carlo, in statistical inference.

gechun liangis a third year DPhil student in the Stochastic Analysis Group of the

Mathematical Institute. His supervisors are Terry Lyons and Zhongmin

Qian. He has a Master’s Degree in Mathematics from Tongji University,

and studied finance as an undergraduate in Jilin University. His research

focuses on stochastic analysis and financial mathematics. Specifically,

he is interested in backward stochastic differential equations and

applications of the cubature method and rough path theory. He is also

interested in credit risk modelling using utility indifference valuation.

1 8

S T U D E n T S 1 9

arnaud lionnetis a DPhil student at the Mathematical Institute and a member of the

Stochastic Analysis Group. His research interests are in the analysis and

application of stochastic models.

diaa noureldin is a DPhil student in Economics at the University of Oxford, where he

also studied for an MPhil in Economics. He holds a Bachelor’s Degree in

Economics from the American University in Cairo.

Diaa’s research interests lie in the field of financial econometrics. His

MPhil thesis focused on modelling the dynamics of the term structure

of interest rates using copula methods. For his DPhil research, he is

currently studying a new class of models for forecasting multivariate

volatility using high-frequency data.

cavit pakelis interested in the field of financial econometrics and, specifically, in

volatility modelling. He is also interested in the nuisance parameter

issue and bias reduction in the likelihood framework. His current

research focuses on elimination of bias in GARCH panels, a model that

enables univariate volatility modelling using a panel of asset returns, as

opposed to considering a single time-series only. As such, this structure

makes it possible to model volatility using a smaller than usual number

of observations in the time-series dimension.

dan wangis a visiting student from the University of Chicago. Her research

interests include stochastic calculus and econometrics.

sumudu watugalais interested in the areas of international finance, financial markets,

contagion, and volatility. Her current work focuses on how interlinks

between countries such as trade and capital flows affect markets

and economies, especially during periods of financial crisis. Her

undergraduate and previous postgraduate study was in computer

science, engineering, and finance at MIT. Sumudu worked in the finance

industry, specialising in volatility and derivatives, prior to joining Oxford

for her doctoral studies.

yuan xia is a DPhil student at the Mathematical Institute. His research interests

include numerical methods in finance, currently focusing on Multilevel

Monte Carlo method for jump processes.

weijun xuis a first year DPhil student in the Stochastic Analysis Group. Before

joining Oxford, he completed a Degree in Economics and Mathematics

at Shanghai Jiaotong University, and a Master’s Degree in Statistics at

Harvard University.

Weijun is interested in various problems in probability theory. His current

interest lies in exploring the relationship between paths and signatures,

analogous to that of functions and their Fourier series. He is currently focused

on inversion of signature.

yifei zhongis a second year DPhil student in the Mathematical and Computational

Finance group at the Mathematical Institute. He completed a Bachelor

of Science Degree at Peking University in China and a Master of

Science degree at the National University of Singapore, specialising

in mathematical finance. Currently, his research is focused on optimal

stopping time and applied partial differential equations. He is also

interested in behavioural finance and time inconsistent problems.

mathematics

FacultyGreg Gyruko, Departmental Lecturer at the Mathematical and

Computational Finance Group, University of Oxford.

Research Interests Rough Paths Based and Probabilistic Numerical

Methods and their Applications in Computational Finance

Michael Monoyios, University Lecturer in Financial Mathematics at the

Mathematical Institute, University of Oxford.

Research Interests Optimal Hedging in Incomplete Markets, Transaction

Costs and Singular Control, Parameter Uncertainty in Investment and

Hedging, Insider Trading and Information Problems

Christoph Reisinger, University Lecturer in Mathematical Finance at the

Mathematical Institute, University of Oxford.

Research Interests Modelling of Financial Markets and the

Development, Analysis and Implementation of Efficient Methods for

Derivative Pricing

Zuoquan Xu, Nomura Junior Research Fellow at the Mathematical

Institute, University of Oxford.

Research Interests Mathematical Finance, Stochastic Control,

Optimisation and Applied PDE

StudentsHoratio Boedihardjo, DPhil Student at the Mathematical Institute,

University of Oxford.

Research Interests Schramm-Loewner Evolution in Riemann Surfaces

Stephen Buckley, DPhil Student in the Stochastic Analysis Group,

University of Oxford.

Research Interests Random Walks in Random Environments

Hualei Chang, DPhil Student in the Mathematical and Computational

Finance Group, Mathematical Institute, University of Oxford.

Research Interests Mathematical Finance, Stochastic Control, Optimisation

Alice Dub, DPhil Student at the Mathematical Institute, University of Oxford.

Research Interests Stochastic Control, in Particular the Merton Problem

of Optimal Investment with Intermediate Consumption

Ni Hao, DPhil Student in the Stochastic Analysis Group, University of Oxford.

Research Interests Rough Paths Theory and Cubature

Xiaodong Luo, DPhil Student at the Oxford Centre for Industrial and

Applied Mathematics, Mathematical Institute, University of Oxford.

Research Interests Nonlinear Time Series Analysis, Nonlinear Dynamical

System Theory, Statistical Modelling and Inference

Wei Pan, DPhil Student in the Stochastic Analysis Group, University of Oxford.

Research Interests Stochastic Analysis and Mathematical Finance, in

Particular Volatility Surface Dynamics Implied by Different Pricing

Frameworks

Jan Tudor, DPhil Student in the Stochastic Analysis Group, University of Oxford.

Research Interests Non-Linear Stochastic Evolution Equations Motivated

by Navier-Stokes

Danyu Yang, DPhil Student at the Mathematical Institute, University of Oxford.

Research Interests Rough Path Theory, Extreme Events in Enforced

Nonlinear Systems, Stochastic Differential Equations

saïd business school

FacultyTim Jenkinson, Professor of Finance at the Saïd Business School,

University of Oxford.

Research Interests Initial Public Offerings, Private Equity, Securitisation,

Regulation and the Cost of Capital

Colin Mayer, Peter Moores Dean and Peter Moores Professor of

Managements Studies at the Saïd Business School, University of Oxford.

Research Interests Corporate Finance, Corporate Governance,

Corporate Taxation, Regulation of Financial Institutions

Thomas Noe, Ernest Butten Professor of Management Studies at the

Saïd Business School, University of Oxford.

Research Interests Corporate Finance, Financial Security Design, Game

Theory, Artificial Agent Economies

engineering science

StudentNauman Shah, DPhil Student in the Pattern Analysis and Machine

Learning Group, Department of Engineering Science, University of Oxford.

Research Interests Analysis of Multivariate Financial Time Series using

a Variety of Pattern Recognition, Signal Processing and Machine

Learning Methods

2 0

A S S o c I AT E m E m B E R S 2 1

law

FacultyWolf-Georg Ringe, DAAD Lecturer in Law at the Institute of European

and Comparative Law, University of Oxford.

Research Interests Law and Finance, Company Law, Conflict of Laws

and European Law

physics

FacultyNick Jones, Systems Biology Fellow at the Department of Physics,

University of Oxford.

Research Interests Non-Trivial Temporal Correlations Present in the

Complex Signals that emerge from Natural Systems and how these

Signals Couple to Underlying Network Dynamics

statistics

FacultyBernard Silverman, Chief Scientific Adviser to the Home Office and a

Professor of Statistics at the University of Oxford.

Research Interests Computational Statistics, Smoothing Methods,

Functional Data Analysis, Multiresolution Analysis in Statistics and the

Analysis of Very High Dimensional Data

StudentCornelius Probst, DPhil Student at the Department of Statistics.

Research Interests Bayesian Statistics under Computational and

Temporal Constraints: Sequential Monte Carlo with Data Streaming

Methods. Topics in Computational Statistics such as GPU computing.

High-Frequency Financial Data such as Limit Order Book Data

computer science

StudentAndrás Salamon, DPhil Student at the Computing Laboratory,

University of Oxford.

Research Interests Constraint Satisfaction, Applications of

Computational Complexity to Quantitative Finance, Limitations of

Parallel Programming

externalNicholas Beale, Managing Director, Sciteb Ltd.

Andrea Calì, Lecturer, Brunel University.

Research Interests Knowledge Representation and Reasoning,

Database Theory, Web Information Systems, Information Integration,

Logics and Databases

Thomas Flury, Quantitative Research Analyst, AHL.

Research Interests Time Series Econometrics, Financial Econometrics

and Parameter Estimation with Particle Filters

Matthias Hagmann-Von Arx, AHL.

Jeremy Large, Research Economist, AHL.

Anthony Ledford, Chief Scientist, AHL.

Research Interests Extreme Value Theory, Modelling Financial

Time Series, Automated Trading and Execution Systems, Market

Microstructure and High Frequency Trading

Asger Lunde, Professor of Economics, School of Economics and

Management, Aarhus University.

Research Interests Time Series Econometrics, Financial Econometrics,

and the Econometrics of Marketing

Andrew Patton, Associate Professor of Economics, Duke University.

Research Interests Financial Econometrics, Forecasting, Volatility and

Dependence Models, Hedge Funds

Torsten Schöneborn, Quantitative Analyst, AHL.

Research Interests Market Microstructure, Optimal Trade Execution,

Optimal Investment under Transaction Costs

Jonathan Tawn, Professor of Statistics at Lancaster University.

Research Interests Extreme Value Theory

2 2 G o v E R n A n c E

advisory board

Rene Carmona is the Paul

M. Wythes ’55 Professor of

Engineering and Finance at

Princeton University.

He is a member of the Department

of Operations Research and Financial

Engineering and Director of Graduate

Studies at the Bendheim Center for Finance.

Peter Carr is a Managing

Director at Morgan Stanley

heading up market modelling.

He is also the Executive Director of the

Master’s in Math Finance program at New

York University’s Courant Institute. Peter

has won awards from Wilmott Magazine

for “Cutting Edge Research’’ and from Risk

Magazine for “Quant of the Year’’.

Robert Engle is the Michael

Armellino Professor of Finance

at New York University Stern

School of Business.

He was awarded the 2003 Nobel Prize

in Economics for his research on the

concept of autoregressive conditional

heteroskedasticity (ARCH).

Hans Föllmer is Professor of

Mathematics at Humboldt

University.

He is renowned for fundamental

contributions to statistical mechanics,

stochastic analysis and mathematical

finance. He has been awarded the Georg

Cantor Medal by the German Mathematical

Society and an honorary doctorate from

the University Paris-Dauphine.

management commitee

Professor Roger Goodman Head of the Social Sciences Division (Chair)

Professor Guy Houlsby Head of Department of Engineering Science

Dr Anthony Ledford Chief Scientist, Man Research Laboratory

Professor Jim Malcomson Head of Department of Economics

Professor Colin Mayer Dean of Saïd Business School

Miss Lucy Mullins Head of Administration, Oxford-Man Institute

Professor Brian Ripley Department of Statistics

Professor Bill Roscoe Director, Oxford University Computing Laboratory

Professor Neil Shephard Director, Oxford-Man Institute

Professor Anne Trefethen Director of Oxford eResearch Centre

Professor Nick Woodhouse Head of Mathematical Institute

c I n D c o n F E R E n c E 2 3

contemporary Issues and new Directions in Quantitative Finance

Inaugural Conference of an Annual Series 9th -11th July 2010

This was the first conference of an Annual Series to be organised by the Institute.

The aim of these conferences is to promote current and emerging

research directions in Quantitative and Mathematical Finance. The

conferences will provide a platform to present new ideas, identify and

formalise new problems, debate about the relevance and importance

of these new areas and plant the seed for innovative interdisciplinary

research collaborations.

The exchange of ideas will benefit, on one hand, mathematicians who

will get exposed to new modelling issues in economics and finance and,

on the other, economists and finance scholars who will learn about

mathematical techniques and approaches for the quantitative problems

emerging in the new models.

The ultimate goal is to create an environment for fruitful and

innovative dialogues among mathematicians, economists and finance

scholars on new ideas and topics.

Every year three themes will be selected. This year the themes were

“Information Percolation in Financial Markets”, “Financial Bubbles”

and “Principal-Agent Problem and Contract Theory”.

Scientific Committee:

R. Carmona

D. Duffie

P-L. Lions

J. Scheinkman

T. Zariphopoulou

X.Y. Zhou

Speakers

Day 1

José Scheinkman (Princeton) “Bubbles and Trading”

Discussion led by Peter Bank (TU Berlin)

Gustavo Manso (MIT) “Information Percolation in Segmented Markets”

Discussion led by Mihai Sirbu (UT Austin)

Day 2

Noah Williams (UW-Madison) “Persistent Private Information”

Discussion led by Huyên Pham (Paris VI-VII)

Harrison Hong (Princeton) “The Disagreement Approach to Asset Pricing”

Discussion led by Chris Rogers (Cambridge)

Darrell Duffie (Stanford) “Information Percolation with Equilibrium

Search Dynamics”

Discussion led by Rene Carmona (Princeton)

Philip Protter (Cornell) “How to Detect an Asset Bubble”

Discussion led by Dmitry Kramkov (Carnegie Mellon)

Day 3

Jaksa Cvitanic (Caltech) “Contract Theory in Continuous Time: An Overview”

Discussion led by Jin Ma (USC)

Irene Gamba (UT-Austin) “Kac N-particle Models for Multi-agent

Interactions in the Dynamics of Information”

Discussion led by Jean-Pierre Fouque (UCSB)

Margaret Meyer (Oxford) “Gaming and Strategic Ambiguity in

Incentive Provision”

Discussion led by Mark Davis (Imperial)

seminar series highlights 20th October 2009, Geert Bekaert (Columbia University) “What Segments Equity Markets?”

Joint with Saïd Business School

Geert Bekaert gave a talk on world equity market segmentation. He

and his co-authors found decreased levels of segmentation in many

developing countries, although the level of segmentation is still

significant. They identify a country’s political risk profile and its stock

market development as two additional local segmentation factors as

well as the US corporate credit spread as a global segmentation factor.

27th October 2009, Walter Schachermayer (University of Vienna) “The Fundamental Theorem of Asset Pricing for Continuous Processes under Small Transaction Costs”

Walter Schachermayer announced a new proof of the classical

Bichteler-Dellacherie theorem and its direct connections to arbitrage.

He and his co-authors constructed a novel proof which is based on

a discrete-time Doob-Meyer decomposition. He also discussed the

connection of these new results to a new characterisation of

semi-martingales in terms of a variant of the so-called ‘no free lunch’,

a fundamental notion in derivative pricing.

30th October 2010, Mark Davis (Imperial College, London) “Jump-Diffusion Risk-Sensitive Asset Management”

Joint with Nomura Centre for Financial Mathematics

Mark Davis and his co-author considered a portfolio optimisation

problem in which asset prices are represented by SDEs driven by

Brownian motion and a Poisson random measure, with drifts that

are functions of an auxiliary diffusion ‘factor’ process. The criterion,

following earlier work by Bielecki, Pliska, Nagai and others, is risk-

sensitive optimisation (equivalent to maximising the expected growth

rate subject to a constraint on variance). By using a change of measure

technique introduced by Kuroda and Nagai, Davis and his co-author

showed that the problem reduces to solving a certain stochastic

control problem in the factor process, which has no jumps. The main

result of the paper is that the Hamilton-Jacobi-Bellman equation for

this problem has a classical solution. The proof uses Bellman’s “policy

improvement” method together with results on linear parabolic PDEs

due to Ladyzhenskaya et al.

5th February 2010, Wei Xiong (Princeton University) “Rollover Risk and Credit Risk”

Joint with Nomura Centre for Financial Mathematics

Wei Xiong and his co-author modelled a firm’s rollover risk generated

by conflict of interest between debt and equity holders. Specifically,

when the firm faces losses in rolling over its maturing debt, its equity

holders are willing to absorb the losses only if the option value of

keeping the firm alive justifies the cost of paying off the maturing

debt. Their model shows that both deteriorating market liquidity and

shorter debt maturity can exacerbate this externality and cause costly

firm bankruptcy at higher fundamental thresholds. Xiong discussed the

implications of this model on liquidity-spillover effects, the flight-to-

quality phenomenon, and optimal debt maturity structures.

2 4

S E m I n A R H I G H l I G H T S 2 5

2nd March 2010, Michael Brandt (Duke University) “What Does Equity Sector Orderflow tell us about the Economy?”

Joint with Saïd Business School

Michael Brandt gave a talk on using empirical measures of portfolio

rebalancing to back out investors’ views, specifically their views about

the state of the economy. He and his co-authors show that aggregate

portfolio rebalancing across equity sectors is consistent with sector

rotation, an investment strategy that exploits perceived differences in

the relative performance of sectors at different stages of the business

cycle. They find that the empirical foot-print of sector rotation has

predictive power for the evolution of the economy, future stock market

returns, and future bond market returns, even after controlling for

relative sector returns. They conclude that, contrary to many theories

of price formation, trading activity contains information that is not

entirely revealed by resulting relative price changes.

11th May 2010, Hélène Rey (London Business School) “Information Asymmetries in Global Institutional Investment”

Joint with Saïd Business School

Hélène Rey spoke on examining the dynamics of international portfolios

with a unique data set on the stock allocations of approximately 6,500

international equity funds domiciled in four different currency areas

during a five year period. She and her co-author find strong support for

portfolio rebalancing behavior aimed at stabilising exchange rate risk

and equity risk exposure around desired levels.

25th May 2010, Ivar Ekeland (University of British Columbia) “Portfolio Management under Time Inconsistency”

Joint with Nomura Centre for Financial Mathematics

Ivar Ekeland gave a talk on portfolio management under time-

inconsistency. This issue is very important in portfolio choice, for there

is strong evidence that individuals discount future utilities at non-

constant rates. The notion of optimality then disappears, because of time

inconsistency and rational behaviour, then, centres around equilibrium

strategies. Ekeland discussed these issues and proposed a solution

approach to a family of investment models with hyperbolic discounting

(the discount rate increases with time). He showed how such discounting

may explain some well-known puzzles in portfolio management.

Full seminar listings can be found at www.oxford-man.ox.ac.uk/events

thomas flury

Learning and Filtering via Simulation: Smoothly Jittered Particle FiltersThomas Flury and Neil Shephard (2009)

Abstract

A key ingredient of many particle filters is the use of the sampling

importance resampling algorithm (SIR), which transforms a sample

of weighted draws from a prior distribution into equally weighted

draws from a posterior distribution. We give a novel analysis of the SIR

algorithm and analyse the jittered generalisation of SIR, showing that

existing implementations of jittering lead to marked inferior behaviour

over the basic SIR algorithm. We show how jittering can be designed

to improve the performance of the SIR algorithm. We illustrate its

performance in practice in the context of three filtering problems.

chris holmes

Two-Sample Bayesian Nonparametric Hypothesis TestingChris Holmes, François Caron, Jim Griffin and David Stephens (2009)

Abstract

In this article we describe Bayesian nonparametric procedures for two-

sample hypothesis testing. Namely, given two sets of samples

and , with , unknown, we wish to evaluate

the evidence for the null hypotheses versus the

alternative H1 : F (1) ≠ F (2) . Our method is based upon a

nonparametric Polya tree prior centered either subjectively or using

an empirical procedure. We show that the Polya tree prior leads to

an analytic expression for the marginal likelihood under the two

hypotheses and hence an explicit measure of the probability of the

null Pr (H0 l { y (1), y (2) } ).

General Bayesian UpdatingChris Holmes and Stephen Walker (2010)

Abstract

Bayesian inference provides a coherent strategy for updating belief

probabilities, but only under certain conditions; which include the well

known problem that observations must be realisations from the prior

probability model. Moreover, only information of a specific type can

be used to update beliefs; namely observations for which a probability

model can be assigned. To address these problems, we propose a general

decision theoretic approach for updating belief probability measures.

This encompasses traditional Bayesian ethodology, as well as other

inference frameworks such as Relative Maximum Entropy, and provides

coherent updates when the Bayesian rule is problematic. We make use

of loss functions for selecting posterior probability measures and indeed

throughout we emphasise the use of loss functions as fundamental

building blocks for inference.

arend janssen

Arbitrage in Order Driven Markets Arend Janssen (2009)

Abstract

In this paper we construct a mathematical model of an order driven

market where traders can submit limit orders and market orders to buy

and sell securities. We adapt the notion of no free lunch of Harrison

and Kreps and Jouini and Kallal to our setting and we proof a no-

arbitrage theorem for the model of the order driven market.

anthony ledford

An Alternative Point Process Framework for Modelling Multivariate Extreme ValuesAlexandra Ramos, Anthony Ledford (2009)

Abstract

Classical techniques for analysing multivariate extremes can often be

framed in terms of the point process representations of de Haan (1985).

Amongst other things, this representation provides a characterisation

of the limiting distribution of the normalised componentwise maxima

of independent and identically distributed unit Fréchet variables, i.e.

the class of multivariate extreme value distributions. The dependence

structures accommodated within this class correspond only to asymptotic

dependence or to exact independence, and so are rather restrictive.

In this paper, an alternative limiting point process representation is

studied that holds regardless of whether the underlying data generation

mechanism is asymptotically dependent or asymptotically independent.

Through the use of the usual pseudo-polar coordinates, we characterise

the intensity function of this point process in terms of the coefficient

of tail dependence h ∈ (0, 1] and a non-negative measure that has

to satisfy a simple normalisation condition but is otherwise arbitrary.

We use this point process representation to derive an analogue of

the standard componentwise maxima result that holds for both

asymptotically dependent and asymptotically independent cases. We

illustrate our results using a flexible parametric example and provide

methods for simulating from both the limiting point process and the

limiting componentwise maxima distribution.

2 6

y (1) iid F (1)

~ y (2) iid F (2)

~ F (1) F (2)

H0 : F (1) = F (2)

=

2 7w o R k I n G PA P E R S

jan obłój

On Azéma-Yor Processes, their Optimal Properties and the Bachelier-Drawdown EquationLaurent Carraro, Nicole El Karoui and Jan Obłój (2009)

Abstract

We study the class of Azéma–Yor processes defined from a general

semimartingale with a continuous running supremum process. We show

that they arise as unique strong solutions of the Bachelier stochastic

differential equation which we prove is equivalent to the Drawdown

equation. Solutions of the latter have the drawdown property: they

always stay above a given function of their past supremum. We then

show that any process which satisfies the drawdown property is in fact

an Azéma–Yor process. The proofs exploit group structure of the set

of Azéma–Yor processes, indexed by functions, which we introduce.

Secondly we study in detail Azéma–Yor martingales defined from

a non-negative local martingale converging to zero at infinity. We

establish relations between Average Value at Risk, Drawdown function,

Hardy-Littlewood transform and its generalised inverse. In particular,

we construct Azéma–Yor martingales with a given terminal law and

this allows us to rediscover the Azéma–Yor solution to the Skorokhod

embedding problem. Finally, we characterise Azéma–Yor martingales

showing they are optimal relative to the concave ordering of terminal

variables among martingales whose maximum dominates stochastically

a given benchmark.

neil shephard

Learning and Filtering via Simulation: Smoothly Jittered Particle FiltersThomas Flury and Neil Shephard (2009)

Abstract

A key ingredient of many particle filters is the use of the sampling

importance resampling algorithm (SIR), which transforms a sample

of weighted draws from a prior distribution into equally weighted

draws from a posterior distribution. We give a novel analysis of the SIR

algorithm and analyse the jittered generalisation of SIR, showing that

existing implementations of jittering lead to marked inferior behaviour

over the basic SIR algorithm. We show how jittering can be designed

to improve the performance of the SIR algorithm. We illustrate its

performance in practice in the context of three filtering problems.

Nuisance Parameters, Composite Likelihoods and a Panel of GARCH ModelsCavit Pakel, Neil Shephard, Kevin Sheppard (2009)

Abstract

We investigate the properties of the composite likelihood (CL) method

for (T x NT ) GARCH panels. The defining feature of a GARCH panel

with time series length T is that, while nuisance parameters are

allowed to vary across NT series, other parameters of interest are

assumed to be common. CL pools information across the panel instead

of using information available in a single series only. Simulations and

empirical analysis illustrate that in reasonably large T CL performs well.

However, due to the estimation error introduced through nuisance

parameter estimation, CL is subject to the “incidental parameter”

problem for small T .

kevin sheppard

Nuisance Parameters, Composite Likelihoods and a Panel of GARCH ModelsCavit Pakel, Neil Shephard, Kevin Sheppard (2009)

Abstract

We investigate the properties of the composite likelihood (CL) method

for (T x NT ) GARCH panels. The defining feature of a GARCH panel

with time series length T is that, while nuisance parameters are

allowed to vary across NT series, other parameters of interest are

assumed to be common. CL pools information across the panel instead

of using information available in a single series only. Simulations and

empirical analysis illustrate that in reasonably large T CL performs well.

However, due to the estimation error introduced through nuisance

parameter estimation, CL is subject to the “incidental parameter”

problem for small T .

john armourArmour, J., Black, B., Cheffins, B. R. and Nolan, R. C., 2010. Private Enforcement of Corporate Law: An Empirical Comparison of the UK and US. Journal of Empirical Legal Studies, 6, pp. 687-722.

Armour, J., Deakin, J., Mollica, V. and Siems, M., 2010. Law and Financial Development: What we are Learning from Time Series Evidence. Brigham Young University Law Review, pp. 1435-1500.

Armour, J., 2009. Enforcement Strategies in UK Corporate Governance: A Roadmap and Empirical Assessment. In Armour, J. and Payne, J. eds. Rationality in Company Law: Essays in Honour of D D Prentice. Oxford: Hart Publishing, pp. 71-119.

Armour, J., 2009. What has the Financial Crisis Taught us About Insolvency Law? In Essers, P., Raaijmakers, G., van der Sangen, G., Verdam, A. and Vermeulen, E., Met Recht: Liber Amicorum for Theo Raaijmakers. Deventer: Kluwer, pp. 1-9.

Armour, J., Deakin, S., Lele, P. and Siems, M., 2009. How Do Legal Rules Evolve? Evidence from a Cross-Country Comparison of Shareholder, Creditor and Worker Protection. American Journal of Comparative Law, 57, pp. 579-629. Winner, European Corporate Governance Institute Prize for ‘Best Law Working Paper 2009’.

Armour, J., Deakin, S., Sarkar, P., Siems, M. and Singh, A., 2009. Shareholder Protection and Stock Market Development: A Test of the Legal Origins Hypothesis. Journal of Empirical Legal Studies, 6, pp. 343-381. Winner, European Corporate Governance Institute Prize for ‘Best Law Working Paper 2008’ for working paper version, ECGI Law WP108/2008.

Armour, J. and Lele, P., 2009. Law, Finance, and Politics: The Case of India. Law and Society Review, 43, pp. 491-526.

Armour, J. and Payne, J. eds., 2009. Rationality in Company Law: Essays in Honour of D D Prentice. Oxford: Hart Publishing.

Kraakman, R., Armour, J., Davies, P. L., Enriques, L., Hansmann, H. B., Hertig, G., Hopt, K. J., Kanda H. and Rock, E. B., 2009. The Anatomy of Corporate Law, 2nd ed, Oxford and New York: OUP.

andrea calìCalì, A. and Martinenghi, D., 2010. Querying Incomplete Data over Extended ER Schemata. Theory and Practice of Logic Programming,10(3), pp. 291-329.

Calì, A. and Martinenghi, D., 2010 Querying the Deep Web. In EDBT, 13th International Conference on Extending Database Technology. Lausanne, Switzerland, 22nd – 26th March 2010. Switzerland: EDBT, pp. 724-727.

Calì, A., Calvanese, D. and Martinenghi, D., 2009. Dynamic Query Optimization under Access Limitations and Dependencies. Journal of Universal Computer Science, 15, pp.33-62.

Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. A General Datalog-Based Framework for Tractable Query Answering over Ontologies. In ACM, 28th Symposium on Principles of Database Systems 2009, Providence, Rhode Island, USA, 29th June-1st July 2009. New York: ACM Press, pp. 77-86.

Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In ACM, Invited paper in the 12th International Conference on Database Theory 2009. St. Petersburg, Russia, 23rd-25th March 2009. New York: ACM Press, pp. 14-30.

Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In SEBD, 11th Italian Symposium on Advanced Database Systems. Camogli, Italy, 21st-24th June 2009. Genova: SEBD.

Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Tractable Query Answering over Ontologies with Datalog±. In CEUR Proceedings Volume 477, 22nd International Workshop on Description Logics 2009, Oxford, UK, 27th-30th July 2009. CEUR Electronic Proceedings Vol. 477. Aachen, Germany: CEUR.

Calì, A., Lukasiewicz, T., Predoiu, L. and Stuckenschmidt, H., 2009. Tightly Coupled Probabilistic Description Logic Programs for the Semantic Web. Journal on Data Semantics, 12, pp. 95-130.

Calì, A. and Torlone, R., 2009. Checking Containment of Schema Mappings (Preliminary Report). In: CEUR Proceedings Volume 450, 3rd Alberto Mendelzon International Workshop on Foundations of Data Management. Arequipa, Peru, 12th -15th May 2009. Aachen, Germany: CEUR.

thomas cassCass, T. and Friz, P., 2010. Densities for Rough Differential Equations under Hörmander’s Condition, Annals of Mathematics, 171, pp. 2115-2141.

Cass, T., Friz, P. and Victoir, N., 2009. Non-Degeneracy of Wiener Functionals arising from Rough Differential Equations, Transactions of the American Mathematical Society, 361 (6), pp. 3359-3371.

thomas fluryShephard, N. and Flury, T., 2010. Bayesian Inference based only on Simulated Likelihood: Particle Filter Analysis of Dynamic Economic Models. Econometric Theory. Forthcoming.

christo fogelbergFogelberg, C. and Palade, V., 2009. Evaluating Clustering Algorithms for Genetic Regulatory Network Structural Inference. In Research and Development in Intelligent Systems XXVI, AI-2009 Proceedings. Cambridge, UK, 15th-17th December 2009. London: Springer-Verlag.

Fogelberg, C., Palade, V., 2009. Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap. In: Abraham, A. et al eds. Foundations of Computational Intelligence, Berlin: Springer-Verlag, pp. 3-34.

mike gilesGiles, M., 2010. Crank-Nicolson Time-Marching. Encyclopedia of Quantitative Finance. UK: John Wiley and Sons.

Lee, A., Yau, C., Giles, M., Doucet, A., Holmes, C., 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods. Journal of Computational and Graphical Statistics. Forthcoming.

Giles, M., 2009. Multilevel Monte Carlo for Basket Options. Winter Simulation Conference 2009. Austin, Texas, 13th – 16th December 2009. USA: WSC.

Giles, M., 2009. ‘Vibrato’ Monte Carlo Method. In Monte Carlo and Quasi-Monte Carlo Methods 2008. Springer, 2009, pp. 369-382.

Giles, M. and Waterhouse, B.J., 2009. Multilevel Quasi-Monte Carlo Path Simulation, In Albrecher, H., Runggaldier, W. And Schachermayer, W. Eds Advanced Financial Modelling: Volume 8 of Radon Series on Computational and Applied Mathematics. Berlin: Walter de Gruyter, pp.165-181.

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georg gottlobCalì, A., Gottlob, G. and Lukasiewicz, T., 2010. Tractable Query Answering over Ontologies with Datalog±. Description Logics. Forthcoming.

Abiteboul, S., Gottlob, G., and Manna, M., 2009. Distributed XML Design. In ACM, 28th Symposium on Principles of Database Systems 2009, Providence, Rhode Island, USA, 29th June-1st July 2009. New York: ACM Press, pp.247-258.

Baumgartner, R., Gottlob, G., Herzog, M., 2009. Web Data Extraction for Online Market Intelligence. In VLDB, 35th International Conference on Very Large Databases, Lyon, France, 24th – 28th August 2009. France: VLDB.

Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. A General Datalog-Based Framework for Tractable Query Answering over Ontologies. In ACM, 28th Symposium on Principles of Database Systems 2009, Providence, Rhode Island, USA, 29th June-1st July 2009. New York: ACM Press, pp. 77-86.

Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Datalog±: A Powerful Family of Languages for Query Answering over Ontologies. In ACM, 12th International Conference on Database Theory 2009. St. Petersburg, Russia, 23rd-25th March 2009. New York: ACM Press.

Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In ICDT, 12th International Conference, St. Petersburg, Russia, March 23rd -25th 2009. Russia: ICDT, pp. 14-30.

Campi, A., Gottlob, G., Hoye, B., 2009. Wormholes of Communication: Interfacing Virtual Worlds and the Real World. In IEEE, 23rd International Conference on Advanced Information Networking and Applications, Bradford, UK, 26th-29th May 2009. UK: IEEE.

Gottlob, G., Greco, G. and Francesco Scarcello, 2009. Tractable Optimization Problems through Hypergraph-Based Structural Restrictions. In ICALP, 36th Internatioanl Colloquium: Automata, Languages and Programming, Rhodes, Greece, 5th-12th July 2009. Berlin: Springer, pp. 16-30.

Gottlob, G., Miklós, Z., and Schwentick, T., 2009. Generalized Hypertree Decompositions: NP-Hardness and Tractable Variants. Journal of the ACM, 56 (6).

Gottlob, G., Pichler, R., and Savenkov, V., 2009. Normalization and Optimization of Schema Mappings. In VLDB, 35th International Conference on Very Large Databases, Lyon, France, 24th – 28th August 2009. France: VLDB.

Gottlob, G., Tien Lee, S. and Valiant, G., 2009. Size and Treewidth Bounds For Conjunctive Queries. In ACM, 28th Symposium on Principles of Database Systems 2009, Providence, Rhode Island, USA, 29th June-1st July 2009. New York: ACM Press, pp. 45-54.

lajos gergely gyurkóGyurkó, L.G. and Lyons, T.J., 2010. Rough Paths Based Numerical Algorithms in Computational Finance. Mathematics in Finance, AMS, Contemporary Mathematics .

Gyurkó, L.G. and Lyons, T.J., 2010. Efficient and Practical Implementations of Cubature on Wiener Space. Stochastic Analysis 2010, London: Springer. Forthcoming.

ben hamblyAleksandrov, N. and Hambly, B., 2010. A Dual Approach to Multiple Exercise Option Problems under Constraints. Mathematical Methods in Operations Research, 71, pp. 503-533.

Hambly, B. and Kumagai, T., 2010. Diffusion on the Scaling Limit of the Critical Percolation Cluster in the Diamond Hierarchical Lattice. Communications in Mathematical Physics, 295, pp. 29-69.

Hambly, B. and Lyons, T., 2010. Uniqueness for the Signature of a Path of Bounded Variation and the Reduced Path Group. Annals of Mathematics, 171, pp. 109-167.

Hambly, B., 2010. Asymptotics for Functions Associated with Heat Flow on the Sierpinski Carpet. Canadian Journal of Mathematics. Forthcoming.

Barlow, M. and Hambly, B., 2009. Parabolic Harnack Inequality and Local Limit Theorem for Percolation Clusters. Electronic Journal of Probability, 14, pp. 1-26.

Hambly, B., Howison, S. and Kluge, T., 2009. Modelling Spikes and Pricing Swing Options in Electricity Markets. Quantitative Finance, 9, pp. 937-949.

vicky hendersonHenderson. V., 2010, Is Corporate Control Effective When Managers Face Investment Timing Decisions in Incomplete Markets? Journal of Economic Dynamics and Control, 34 (6), pp. 1062-1076.

Henderson, V. and Hobson, D., 2010. Optimal Liquidation of Derivative Portfolios. Mathematical Finance. Forthcoming.

Grasselli, M. and Henderson, V., 2009. Risk Aversion and Block Exercise of Executive Stock Options. Journal of Economic Dynamics and Control, 33, pp.109-127.

Henderson, V. and Hobson, D., 2009. Utility Indifference Pricing - An Overview. In R. Carmona, ed. Indifference Pricing: Theory and Applications, Princeton: Princeton University Press, pp.44-74.

chris holmesLee, A. and Holmes, C., 2010. Discussion of Particle Markov Chain Monte Carlo Methods by C. Andrieu, A. Doucet and R. Holenstein. Journal of the Royal Statistical Society: Series B, 72 (3), pp. 269-342.

Lee, A., Yau, C., Giles, M., Doucet, A. and Holmes, C., 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods. Journal of Computational and Graphical Statistics. Forthcoming.

Yau, C., Papaspiliopoulos, O., Roberts, G.O., Holmes, C., 2010. Bayesian Nonparametric Hidden Markov Models. Journal of the Royal Statistical Society: Series B. Forthcoming.

Anjum, S., Doucet, A. and Holmes C., 2009. A Boosting Approach to Structure Learning of Graphs with and without Prior Knowledge. Bioinformatics 25, pp. 2929-2936.

Antonyuk, A. and Holmes, C . 2009. On Testing for Genetic Association in Case-Control Studies when Population Allele Frequencies are Known. Genetic Epidemiology. 33, pp. 371-378.

Holmes, C and Jasra, A., 2009. Antithetic Methods for Gibbs Sampling. Journal of Computational and Graphical Statistics. 18, pp. 401-414.

Klingelhoefer, J., Moutsianas, L. and Holmes, C., 2009. Approximate Bayesian Feature Selection on a Large Meta-Dataset Offers Novel Insights on Factors that Effect siRNA Potency. Bioinformatics, 25, pp. 1594-1601.

Lemieux, J., Gomez-Escobar, N., Feller, A., Carret, C., Amambua-Ngwa , A., Pinches, R., Day, F., Kyes, S., Conway, D., Holmes, C. and Newbold, C., 2009. Statistical Estimation of Cell-Cycle Progression and Lineage Commitment in ‘Plasmodium Falciparum’ Reveals a Homogeneous Pattern of Transcription in ‘ex vivo’ Culture. National Academy of Sciences, 5th May 2009, 106 (18), pp.7559-7564.

Webb, A., Hancock, J. and Holmes, C . 2009. Phylogenetic Inference under Recombination using Bayesian Stochastic Topology Selection. Bioinformatics, 25, pp. 197-203.

sam howisonCartea, A. and Howison, S., 2009. Option Pricing with Levy-Stable Processes Generated by Levy-Stable Integrated Variance. Quantitative Finance, 9, pp.397-409.

Coulon, M. and Howison, S., 2009. Stochastic Behaviour of the Electricity Bid Stack: From Fundamental Drivers to Power Prices. Journal of Energy Markets, 2, pp. 29-69.

Fenn, D., Howison, S., Johnson, N.F. and Williams, S., 2009. The Mirage of Triangular Arbitrage in the Spot Foreign Exchange Market. International Journal of Theoretical Applied Finance. 12, pp. 1105-1123.

Hambly, B., Howison, S. and Kluge, T., 2009. Modelling Spikes and Pricing Swing Options in Electricity Markets. Quantitative Finance, 9, pp. 937-949.

tim jenkinsonJenkinson, T.J., 2009. Private Equity. In the EEAG Report on the European Economy.

Jenkinson, T.J. and Jones, H., 2009. Competitive IPOs. European Financial Management. 15 (4), pp. 733-756

Jenkinson, T.J. and Jones, H., 2009. IPO Pricing and Allocation: a Survey of the Views of Institutional Investors. The Review of Financial Studies, 22, pp.1477-1504.

nick jonesAgarwal, S., Deane, C., Porter, M. and Jones N., 2010. Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks. PLoS Computational Biology, 6 (6). Lewis, A., Jones, N., Porter, M. and Deane, C., 2010. The Function of Communities in Protein Interaction Networks at Multiple Scales. BMC Systems Biology, 4.

Little, M. and Jones, N., 2010. Sparse Bayesian Step-Filtering for High-Throughput Analysis of Molecular Machine Dynamics. In IEEE, ICASSP 2010. Dallas, Texas, 14th-19th March 2010. USA: IEEE.

Staniczenko, P., Lewis, O., Jones, N. and Reed-Tsochas, F., 2010. Structural Dynamics and Robustness of Food Webs. Ecology Letters, 13, pp.891-899.

Fenn, D. J., Porter, M. A., McDonald, M., Williams, S., Johnson, N. F. and Jones, N. S., 2009. Dynamic Communities in Multichannel Data. Chaos, 19.

Smith, D., Onnela, J. and Jones, N., 2009. Master-Equation Analysis of Accelerating Networks. Physical Review E, 79.

Staniczenkko, P., Lee, C. and Jones, N. S., 2009. Rapidly Detecting Disorder in Rhythmic Biological Signals. Physical Review E, 79.

jeremy largeLarge, J., 2010. Estimating Quadratic Variation when Quoted Prices Change by a Constant Increment. Journal of Econometrics. Forthcoming.

Large, J., 2009. A Market-Clearing Role for Inefficiency on a Limit Order Book. Journal of Financial Economics, 91, pp.102-117.

ada lauLau, A. and McSharry, P., 2010. Approaches for Multi-Step Density Forecasts with Application to Aggregated Wind Power. Annals of Applied Statistics. Forthcoming.

anthony leeLee, A. and Holmes, C., 2010. Discussion of Particle Markov Chain Monte Carlo Methods by C. Andrieu, A. Doucet and R. Holenstein. Journal of the Royal Statistical Society: Series B, 72 (3), pp. 269-342.

Lee, A., Yau, C., Giles, M., Doucet, A., Holmes, C., 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods. Journal of Computational and Graphical Statistics. Forthcoming.

anthony ledfordRamos, A. and Ledford, A., 2010. An Alternative Point Process Framework for Modelling Multivariate Extreme Values. Communications in Statistics – Theory and Methods. Forthcoming.

Ramos, A. and Ledford, A., 2009. A New Class of Models for Bivariate Joint Tails. Journal of the Royal Statistical Society: Series B, 71, Part 1, pp.219-241.

gechun liangLiang, G., Lin, J., Wu, S. and Zheng, H., 2010. The Valuation of the Basket CDSs in a Primary-Subsidiary Model. Asia Pacific Journal of Operational Research. Forthcoming

christian littererLitterer, C. and Lyons, T., 2010. Introducing Cubature to Filtering. In D. Crisan and B. Rozovsky, eds. Oxford Handbook of Non-Linear Filtering. Oxford: Oxford University Press. Forthcoming.

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asger lundeBarndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., 2010. Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading. Journal of Econometrics. Forthcoming.

Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., 2010. Subsampling Realised Kernels. Journal of Econometrics. Forthcoming.

Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N.S., 2009. Realised Kernels in Practice: Trades and Quotes. Econometrics Journal. 2009, 12, 3, pp.C1-C32.

Lunde, A. and Zebedee, A., 2009. Intraday Volatility Responses to Monetary Policy Events. Financial Markets and Portfolio Management, 23, pp. 383-399

terry lyonsHambly, B. and Lyons, T., 2010. Uniqueness for the Signature of a Path of Bounded Variation and the Reduced Path Group. Annals of Mathematics, 171, pp. 109-167.

Litterer, C. and Lyons, T., 2010. Introducing Cubature to Filtering. In D. Crisan and B. Rozovsky, eds. Oxford Handbook of Non-Linear Filtering. Oxford: Oxford University Press. Forthcoming.

Qian, Z., Liang, G. and Lyons, T., 2010. Backward Stochastic Dynamics on a Filtered Probability Space. The Annals of Probability. Forthcoming.

robert mayMay, R and Arinaminpathy, N., 2010. Systemic Risk: the Dynamics of Model Banking Ecosystems. Journal of the Royal Society: Interface, 6, pp. 823-838.

michael monoyiosDanilova, A, Monoyios, M and Ng, A. 2010. Optimal Investment with inside Information Parameter Uncertainty. Mathematics and Financial Economics, 3, pp. 13-38.

Monoyios, M., 2009. Optimal Investment and Hedging Under Partial and Inside Information. H Albrecher, W Runggaldier and W Schachermayer, eds. Advanced Financial Modelling, Berlin: Walter de Gruyter.

per myklandLin, M., Mykland, P.A. and Chen, R., 2010. On Generating Monte Carlo Sample of Continuous Diffusion Bridges. Journal of the American Statistical Association, 105, pp. 820-838.

Ait-Sahalia, Y., Mykland, P. and Zhang, L., 2010. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise. Journal of Econometrics. Forthcoming.

Mykland, P., 2010. A Gaussian Calculus for Inference from High Frequency Data. Annals of Finance. Forthcoming.

Mykland, P. and Zhang, L., 2010. The Econometrics of High Frequency Data. In M. Kessler, A. Lindner and M. Sorensen eds. Statistical Methods for Stochastic Differential Equations. London: Chapman and Hall. Forthcoming.

Zhang, L., Mykland, P.A. and Ait-Sahalia, Y., 2010. Edgeworth Expansions for Realized Volatility and Related Estimators. Journal of Econometrics. Forthcoming.

Ait-Sahalia, Y. and Mykland, P.A., 2009. Estimating Volatility in the Presence of Market Microstructure Noise: A Review of the Theory and Practical Considerations. In T. Andersen, R. Davis, J.P. Kreiss, and T.H. Mikosch, eds., Handbook of Financial Time Series. Berlin: Springer-Verlag, pp. 577-598.

Jacod, J., Li, Y., Mykland, P.A., Podolskij, M. and Vetter, M., 2009. Microstructure Noise in the Continuous Case: The Pre-Averaging Approach. Stochastic Processes and Applications, 119, pp. 2249-2276.

Mykland, P.A., 2009. Options Pricing Bounds and Statistical Uncertainty. In Y. Ait-Sahalia and L.P. Hansen, eds. Handbook of Financial Econometrics. Oxford and Amsterdam: North-Holland, pp. 135-195.

Mykland, P.A. and Zhang, L., 2009. Inference for Continuous Semimartingales Observed at High Frequency. Econometrica, 77 (5), pp.1403-1445.

sergey nadtochiy

Carmona, R. and Nadtochiy, S., 2010. Tangent Levy Market Models. Finance and Stochastics. Forthcoming.

Carmona, R. and Nadtochiy, S., 2010. Tangent Models as a Mathematical Framework for Dynamic Calibration, IJTAF. Forthcoming.

Carmona, R. and Nadtochiy, S., 2009. Local Volatility Dynamic Models. Finance and Stochastics, 13, pp. 1-48.

thomas noeNoe, T., Khanna, N. and Sonti, R., 2009. Good IPOs Draw in Bad: Inelastic Banking Capacity and Hot Markets. Review of Financial Studies, 21, pp.1873-1906.

jan obłójObłój, J., 2010.The Skorokhod Embedding Problem and its Applications in Mathematical Finance. In Cont, R. ed., Encyclopedia of Quantitative Finance. UK: John Wiley and Sons, pp. 1653-1657.

Obłój, J. and Cox, A.M.G., 2010. Robust Hedging of Double No-Touch Barrier Options. Finance and Stochastics. Forthcoming.

Obłój, J., Cox, A.M.G. and Hobson, D., 2010. Time-Homogeneous Diffusions with a Given Marginal at a Random Time. ESAIM Probability and Statistics (Special Volume in Honour of Marc Yor). Forthcoming.

Obłój, J. and Pistorius, M., 2009. On an Explicit Skorokhod Embedding for Spectrally Negative Lévy Processes. Journal of Theoretical Probability, 22 (2), pp. 418-440.

cavit pakelPakel, C., Shephard N. and Sheppard K., 2010. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models. Statistica Sinica. Forthcoming.

andrew pattonPatton, A. and Timmermann, A., 2010. Generalized Forecast Errors, a Change of Measure, and Forecast Optimality. In T. Bollerslev, J.R. Russell and M.W. Watson, eds. Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle, Oxford: Oxford University Press.

Patton, A., 2010. Volatility Forecast Comparison using Imperfect Volatility Proxies. Journal of Econometrics. Forthcoming.

Patton, A. and Timmerman, A., 2010. Monotonicity in Asset Returns: New Tests with Applications to the Term Structure, the CAPM and Portfolio Sorts. Journal of Financial Economics. Forthcoming.

Patton, A., and Timmermann, A., 2010, Predictability of Output Growth and Inflation: A Multi-Horizon Survey Approach. Journal of Business and Economic Statistics. Forthcoming.

Patton, A., and Timmermann, A., 2010, Why do Forecasters Disagree? Lessons from the Term Structure of Cross-Sectional Dispersion. Journal of Monetary Economics. Forthcoming.

Patton, A., 2009. Are ‘Market Neutral’ Hedge Funds Really Market Neutral? Review of Financial Studies, 22, pp. 2495-2530.

Patton, A., 2009. Copula-Based Models for Financial Time Series. In T.G. Andersen, R.A. Davis, J.P. Kreiss and T. Mikosch, eds. Handbook of Financial Time Series. Berlin: Springer-Verlag, pp. 767-785.

Patton, A. and Sheppard, K., 2009. Evaluating Volatility and Correlation Forecasts. In T.G. Andersen, R.A. Davis, J.-P. Kreiss and T. Mikosch, eds. Handbook of Financial Time Series. Berlin: Springer-Verlag, pp. 801-838.

Patton, A. and Sheppard, K., 2009. Optimal Combinations of Realised Volatility Estimators. International Journal of Forecasting, 25 (2), pp. 218-238.

zhongmin qianQian, Z. and Chen, G. Q., 2010. A study of the Navier-Stokes Equations with the Kinematic and Navier Boundary Conditions. Indiana University Mathematics Journal. Forthcoming.

Qian, Z., Liang, G. and Lyons, T., 2010. Backward Stochastic Dynamics on a Filtered Probability Space. The Annals of Probability. Forthcoming.

Qian, Z., 2009. An Estimate for the Vorticity of the Navier-Stokes Equation. Comptes Rendus Mathematique, 347, pp.89-92.

Qian, Z., 2009. Ricci Flow on a 3-Manifold with Positive Scalar Curvature. Bulletin de la Société Mathématique de France, 133, pp. 145-168.

Qian, Z., Chen, G. Q. and Osborn, D., 2009. The Navier-Stokes Equations with the Kinematic and Vorticity Boundary Conditions on Non-Linear Boundaries. Acta Mathematica Scientia, 29 (4), pp.919-948.

tarun ramadoraiRamadorai, T., 2010. Institutional Investors, in H. Kent Baker and J. Nofsinger eds. Behavioral Finance: Investors, Corporations, and Markets. Hoboken, NJ: John Wiley and Sons. Forthcoming.

Ramadorai, T., 2010. The Secondary Market for Hedge Funds and the Closed Hedge Fund Premium. Journal of Finance. Forthcoming.

wolf-georg ringeRinge, W.G., 2010. Company Law and Free Movement of Capital, Cambridge Law Journal, 69, pp.378-409.

Ringe, W.G., 2010. Sparking Regulatory Competition in European Company Law - The Impact of the Centros Line of Case-Law and its Concept of ‘Abuse of Law’. In R. de la Feriaand and S. Vogenauer eds. Prohibition of Abuse of Law - A New General Principle of EU Law. Oxford: Hart Publishing. Forthcoming.

Ringe, W.G, Gullifer, L. and Théry, P. eds., 2009. Current Issues in European Financial and Insolvency Law - Perspectives from France and the UK. Oxford and Portland, Oregon: Hart Publishing.

stephen robertsGarnett, R., Osborne, M., Reece, S., Rogers, A. and Roberts, S., 2010. Sequential Bayesian Prediction in the Presence of Changepoints and Faults. The Computer Journal.

Garnett, R., Osborne, M. and Roberts, S., 2010. Bayesian Optimization for Sensor Set Selection, In IPSN 2010, 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, Stockholm, Sweden, 12th-16th April 2010. New York: ACM Press.

Kaufman, M., and Roberts, S., 2010. Coordination vs. Information in Multi-agent Decision Processes. In AAMAS 2010, 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto Canada 10th-14th May 2010. Canada: AAMAS.

Lowne, D., Roberts, S. and Garnett, R., 2010. Sequential Non-stationary Dynamic Classification with Sparse Feedback. Pattern Recognition, 43 (3), pp. 897-905.

McInerney, R., Roberts, S. and Rezek, I., 2010. Sequential Bayesian Decision Making for Multi-armed Bandit. In AAMAS 2010, 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto Canada 10th-14th May 2010. Canada: AAMAS.

Osborne, M., Garnett, R. and Roberts, S., 2010. Active Data Selection for Sensor Networks with Faults and Changepoints. In IEEE, 24th International Conference on Advanced Information Networking and Applications, Perth, Australia, 20th-23rd April 2010. Australia: IEEE.

Reece, S. and Roberts, S., 2010. An Introduction to Gaussian Processes for the Kalman Filter Expert. In Fusion 2010, 13th International Conference on Information Fusion, Edinburgh, UK, 26th-29th July 2010. UK: Fusion.

Reece, S. and Roberts, S., 2010. The Near Constant Acceleration Gaussian Process Kernel for Tracking. IEEE Signal Processing Letters, 17 (8), pp. 707-710.

Yoon, J., and Roberts, S., 2010. Robust Measurement Validation in Target Tracking using Geometric Structure. IEEE Signal Processing Letters, 17 (5), pp. 493-496.

Lee, S. and Roberts, S. 2010. Sequential Dynamic Classification Using Latent Variable Models. The Computer Journal 2010. Forthcoming.

Mann, R., Freeman, R., Osborne, M., Garnett, R., Meade, R., Armstrong, C., Biro, D., Guilford, T, and Roberts, S., 2010. Gaussian Processes for Prediction of Homing Pigeon Flight Trajectories. In AIP 29th Bayesian Inference and Maximum Entropy Methods in Science and Engineering Oxford, USA 5th-10th July 2009. Forthcoming.

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Ebden, M., Stranjak, A. and Roberts, S. 2009. Visualizing Uncertainty in Reliability Functions with Application to Aero Engine Overhaul. Journal of the Royal Statistical Society C, 59 (1), pp. 163-173.

Garnett, R.,Osborne, M. and Roberts, S., 2009. Sequential Bayesian Prediction in the Presence of Changepoints. In 26th International Conference on Machine Learning, Montreal, Canada, 14th-18th June 2009. ACM International Conference Proceeding Series, 382. New York: ACM Press.

Osborne, M., Garnett, R. and Roberts, S., 2009. Gaussian processes for Global Optimization. In 3rd International Conference on Learning and Intelligent Optimization, Trento, Italy 18th-22nd January 2009.

Reece, S., Roberts, S., Claxton, C., and Nicholson, D., 2009. Multi-Sensor Fault Recovery in the Presence of known and unknown Fault Types. In Fusion 2009, 12th International Conference on Information Fusion, Seattle, Washington, 6th-9th July 2009. USA: Fusion.

Tsui, C., Gan, J. Q., and Roberts, S., 2009. A Self-paced Brain-Computer Interface for Controlling a Robot Simulator: An Online Event Labelling Paradigm and an Extended Kalman Filter Based Algorithm for Online Training. Medical and Biological Engineering and Computing, 47 (3), pp. 257-265.

Yoon, J., Roberts, S., Dyson, M., and Gan, J., 2009. Adaptive Classification for Brain Computer Interface Systems using Sequential Monte Carlo Sampling. Neural Networks: Special Issue on Brain Machine Interfaces, 22, pp. 1286-1294.

andrás salamonCooper, M. C., Jeavons, P. G. and Salamon, A. Z., 2010. Generalizing Constraint Satisfaction on Trees: Hybrid Tractability and Variable Elimination. Artificial Intelligence, 174 (9-10), pp. 570-584.

torsten schönebornSchöneborn, T., 2010. A guided tour of new results on Trade Execution in Illiquid Markets. Blaetter der DGVFM, 31 (1), pp. 79-90.

Schied, A., Schöneborn, T. and Tehranchi, M., 2010. Optimal Basket Liquidation for CARA Investors is Deterministic. Applied Mathematical Finance. Forthcoming.

Schied, A. and Schöneborn, T., 2009. Risk Aversion and the Dynamics of Optimal Liquidation Strategies in Illiquid Markets. Finance and Stochastics, 13 (2), pp. 181-204.

neil shephardBarndorff-Nielsen, O.E., Kinnebrock, S. and Shephard, N., 2010. Measuring Downside Risk - Realised Semivariance. In T. Bollerslev, J. Russell and M. Watson eds., Edited Volume in honour of Robert F. Engle. Oxford: Oxford University Press, pp. 117-136..

Shephard, N., 2010. Deferred Fees for Universities, Economic Affairs, 30 (2), pp. 40-44.

Shephard, N., 2010. Deferred Fees for Universities. Submission to 2nd Round of the Lord Browne Review on Higher Education Funding and Student Finance.

Shephard, N., 2010. Modelling and Measuring Volatility. Encyclopedia of Quantitative Finance. UK: John Wiley and Sons.

Shephard, N., 2010. Submission to Higher Education Funding and Student Finance, 1st Round of the Lord Browne Review.

Shephard, N. and Sheppard, K., 2010. Realising the Future: Forecasting with High Frequency based Volatility (HEAVY) models. Journal of Applied Econometrics, 25 (2), pp. 197-231.

Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., 2010. Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading. Journal of Econometrics. Forthcoming.

Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., 2010. Subsampling Realised Kernels. Journal of Econometrics. Forthcoming.

Pakel, C., Shephard N. and Sheppard K., 2010. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models. Statistica Sinica. Forthcoming.

Shephard, N. and Flury, T., 2010. Bayesian Inference based only on Simulated Likelihood: Particle Filter Analysis of Dynamic Economic Models. Econometric Theory. Forthcoming.

Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N.S., 2009. Realised Kernels in Practice: Trades and Quotes. Econometrics Journal, 12 (3), pp. C1-C32.

Castle, J.L. and Shephard, N., eds., 2009. The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry. Oxford University Press.

Koopman, S.J., Shephard, N. and Creal, D., 2009. Testing the Assumptions behind Importance Sampling. Journal of Econometrics, 149, pp.2-11.

Shephard, N. and Andersen, T.G., 2009. Stochastic Volatility: Origins and Overview. In T.G. Andersen, R.A. Davis, J.-P. Kreiss and T. Mikosch, eds. Handbook of Financial Time Series, Springer, pp. 233-254.

kevin sheppardPakel, C., Shephard N. and Sheppard K., 2010. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models. Statistica Sinica. Forthcoming.

Patton, A. and Sheppard, K., 2009. Evaluating Volatility and Correlation Forecasts. In T.G. Andersen, R.A. Davis, J.-P. Kreiss and T. Mikosch, eds. Handbook of Financial Time Series, Berlin: Springer Verlag, pp. 801-838.

Patton, A. and Sheppard, K., 2009. Optimal Combinations of Realised Volatility Estimators. International Journal of Forecasting, 25 (2), pp. 218-238.

pierre tarrèsBenaïm, M. and Tarrès, P., 2010. Dynamics of Vertex-Reinforced Random Walks. Annals of Probability. Forthcoming.

mungo wilsonWilson, M. and Pollet, J., 2010. Average Correlation and Stock Market Returns. Journal of Financial Economics. Forthcoming.

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xuoquan xuDai, M., Xu, Z. and Zhou, X., 2010. Continuous-Time Markowitz’s Model with Transaction costs. SIAM Journal on Financial Mathematics, 1, pp. 96-125.

Dai, M. and Xu, Z., 2010. Optimal Redeeming Strategy of Stock Loans with Finite Maturity. Mathematical Finance. Forthcoming.

Shiryaev, A., Xu, Z., Zhou, X., 2009. Thou Shalt Buy and Hold. Quantitative Finance, 8, pp. 765-776.

thaleia zariphopoulouZariphopoulou, T. and Sircar, R., 2010. Utility Valuation of Credit Derivatives and Applications to CDOs. Quantitative Finance, 10, pp. 195-208.

Zariphopoulou, T. and Zitkovic, G., 2010. Maturity-Independent Risk Measures, SIAM Journal on Financial Mathematics, 1, pp. 266-288.

Zariphopoulou, T. and Musiela, M., 2010. Initial Investment Choice and Optimal Future Allocations under Time-Montone Investment Performance Criteria, International Journal of Theoretical and Applied Finance. Forthcoming.

Zariphopoulou, T. and Musiela, M., 2010. Portfolio Choice under Space-Time Monotone Performance Criteria , SIAM Journal on Financial Mathematics. Forthcoming.

Zariphopoulou, T. and Musiela, M., 2010. Stochastic Partial Differential Equations and Portfolio Choice, In C. Chiarella and A. Novikov eds., Contemporary Quantitative Finance: Essays in Honour of Eckhard Platen. London: Springer. Forthcoming.

Zariphopoulou, T., Musiela, M. and Sokoloca, K., 2010. Indifference Valuation in Incomplete Binomial Models. Mathematics in Action. Forthcoming.

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man Group plc

About AHL

AHL is a world-leading quantitative investment manager with an extensive

history of performance and innovation. A pioneer in the application of

systematic trading, we have been serving institutional and private clients

since 1987 and currently manage funds of over USD 20 billion*.

Man provides AHL with centralised product structuring, distribution,

client service and operational support. This allows AHL to focus

exclusively on research and trading model development. This unique

position gives AHL far greater transparency and regulatory oversight

than privately owned managers.

AHL is based in London, Oxford and Hong Kong. Our Oxford office is

home to the Man Research Laboratory (MRL) which is co-located with

the Oxford-Man Institute of Quantitative Finance (OMI) – our unique

collaboration with Oxford University.

AHL Oxford, MRL

The combined MRL-OMI working environment in Oxford has been

purpose-designed to encourage frequent interaction between the two

groups of researchers. AHL and the University aim to create a stimulating

environment of research and innovation where ideas flourish and

practitioners from a wide spectrum of disciplines can bring their skills into

collaboration and learn from each other.

Although AHL Oxford and the Institute have independent aims and

separate research programs, the regular contact and dialogue between

them has significantly benefitted both parties. “The interaction between

our Research Lab and the Institute has put us at the cutting edge in our

field. Looking at what we’ve already achieved, we’re really excited about

the prospects for the future.” Anthony Ledford, Chief Scientist, AHL.

AHL Oxford has grown significantly since we opened in 2007 and has become

a vibrant and thriving part of AHL’s wider research group. We have continued

our long standing principle of recruiting exceptionally strong researchers from

both industry and quantitative academic disciplines including mathematics,

statistics, engineering, computing, the applied sciences and econometrics.

AHL Oxford’s research output has made significant commercial impacts within

AHL’s business, spanning everything from algorithms that automatically check

high-frequency market data through to new trading and order execution

models that manage billions of dollars.

Man is a world-leading alternative investment

management business that is listed in the FTSE

100 Index (EMG). With a broad range of funds for

institutional and private investors globally, we are

known for performance, innovative product design

and investor service.

Our strategy is to offer a broad range of robust alternative investment

products to private investors and institutions worldwide. Strong long-

term performance and our 20+ year track record are key to attracting

and retaining these investors. We serve two principle markets: Private

investors and Institutions

Our global scale differentiates our business, and our key areas of

expertise include people, information technology and risk management.

Our investment management expertise extends from single managers

such as Man AHL to fund of funds managers such as Man Multi-Manager.

* FUM USD21.2 billion as at 30 June 2010

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