S E P T 1 0 A U G 1 1

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 that has generously

provided our core funding for the period

2007-2015, and more generally for its

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

In this report I am pleased to be able to share with you details

of the research interests of the Institute’s members, associate

members and students. OMI is fast gaining a reputation as a

global centre of excellence in the study of quantitative finance

and alternative investments, and the work of our colleagues in

this field continues to draw keen interest from academics and

industry practitioners around the world.

In the subsequent pages you will have an opportunity to learn

more about our members, and this year we have included

extended discussion on the research of three colleagues; Tarun

Ramadorai (page two) discusses his interest in international

financial contagion, whilst articles on Ben Hambly (page six) and

Kevin Sheppard (page four) explain their respective research

concerning the volatility of commodities and financial markets.

OMI’s events programme is an important facet of the Institute,

enabling members and students to better position themselves

to contribute to the development of quantitative finance. Over

the past year we have hosted numerous seminars, conferences

and workshops, as well as a Summer School. We also initiated

a series of thematic workshops preceded by relevant tutorials

to provide our students with the opportunity to broaden and

increase their knowledge of specific subjects. Details of some of

these events can be found in this report.

The past academic year has not only proven to be very

successful, but it has been a time of change for OMI. In July our

founding Director, Professor Neil Shephard stepped down and

Professor Terry Lyons was appointed as his successor.

I speak on behalf of all my colleagues when I say it is an

emotional moment to see Neil stepping down as Director, and

we are all very grateful for the work he has completed over the

last four years. His vision and leadership have been instrumental

in securing OMI’s current status and success. We are very

pleased that he will continue to be involved in the Institute in

his new role as leader of Financial Econometrics and Statistics.

We are also delighted to welcome Terry Lyons as the new

Director. Terry has been with OMI since its inception and has

played an active role in the Institute’s life, supervising students,

organising seminars, serving on the Executive Committee and

integrating the stochastic analysis group. I know that Terry

is delighted to be the Institute’s new Director and is looking

forward to building on OMI’s global reputation.

It is very difficult to capture the contributions, scientific value,

dedication and academic stature of both Neil and Terry, but we

have included a dedicated insert in this report which we hope

will provide some insight into both Directors’ involvement in

the Institute.

I would like to take this opportunity, on behalf

of all my colleagues, to thank Man Group Plc

for their continued funding and support

– without which, the progress highlighted

herein would not have been possible.

Thaleia Zariphopoulou

man Professor of Quantitative Finance

August 2011

Welcome to the fourth Annual Report from the Oxford-Man Institute (OMI), highlighting

current research activities and the progress that has been made developing interaction

among research disciplines.

1

INTRO

TA R U n R A m A d o R A i

The old maxim has never seemed more accurate:

when the US sneezes, the rest of us catch a

cold. What starts out as a blip in an obscure

derivatives market can quickly spiral into a

financial crisis that affects everyone, from the

biggest governments to the poorest citizens.

We need to understand how these panics

spread to prevent them in the future. And that’s

where thinkers like Tarun Ramadorai come in.

An economist by training, Ramadorai is a Reader at the Saïd

Business School and a member of the Oxford-Man Institute.

He spends his professional life thinking about questions that

combine theoretical complexity with immediate relevance to

policy and regulation.

The study of international financial contagion has been one of

his major interests in recent years. He wants to understand how

a crisis moves from market to market, often striking in areas with

no obvious connection to the original source of the problem.

After the financial turmoil of recent years, it’s an immensely

topical subject. It helps explain how panic rippled out from a

downturn in the US housing market to strike at great swathes

of the international financial system, endangering major banks

and insurers, and ultimately even disrupting governments’

ability to borrow.

Ramadorai believes he’s uncovered some of the hidden conduits

which transmit financial stress around the world. One of them

turns out to be emerging market investment funds based in

major financial centres. When there are unexpected losses in

these centres, frightened investors pull money out of all risky

assets, including emerging market funds. Funds must liquidate

assets to raise the cash to pay these withdrawals, so stock

exchanges thousands of miles away suffer a wave of selling,

even though the original trouble was completely unconnected.

Because these markets tend to be comparatively illiquid, with low

daily trading volumes, this selling has a disproportionate impact.

“You wouldn’t expect that India and China would suffer so badly when London and New York did, but that was what happened during the last crisis,” Ramadorai comments. “Paradoxically, this kind of fire-sale activity seems to hit bigger and more liquid emerging markets the hardest – precisely because of their liquidity, fund managers try to sell more assets there.”

The research even suggests ways to predict where the risk is

greatest, by examining where emerging market investors own

a lot of assets in common. For example, if UK-based funds that

are heavily invested in India also own lots of shares in Egypt,

then both will suffer when London does, while other countries

nearby may remain relatively untouched.

“It’s a real concern; the idea of spreading your investments between different markets is to reduce risk through diversification, but it turns out that by buying into these markets, these investors are actually creating a new source of correlation between them,” says Ramadorai.

The effects are predictable and tradeable. The research suggests

an investor who followed the strategy rigorously could have

made high risk-adjusted returns over recent years. The findings

may be even more relevant to regulators as Ramadorai believes

restricting these vectors of financial contagion could help them

control the spread of panic in a crisis.

His other long-term interest lies in hedge funds. These are

among the big financial success stories of recent decades;

they aim to beat the market with flexibility and sophisticated

trading techniques. Once thought of as the preserve of the

ultra-rich, in recent years hedge funds’ popularity has grown

rapidly to take in pension funds, asset managers, university

endowments and even private savers.

This widening appeal has brought vast amounts of new cash

into the industry, but it also creates risk. Wealthy hedge fund

investors can probably look after themselves, but newer

investors may be less sophisticated and need

more protection from regulators.

containing contagion

2

Hedge funds are no longer peripheral market players; they are

a major component of the international financial system. A

big hedge fund collapse could now have serious consequences

for the wider economy. But there’s still little information

available about what these funds are doing to get their

returns. What risks are they running, and how well do their

investors understand them? Ramadorai and his co-author have

developed a way to find out.

“Hedge funds are very frequent traders, but their reporting is almost entirely voluntary and even those that do provide information usually only do so quarterly,” Ramadorai explains. On top of that, they need to report only long positions; short bets go undisclosed. “We wanted to get a sense of what they do over much shorter periods, like a single day on which the markets crash,” he adds.

The solution is to use statistical analysis to analyse readily

available data on general market trends alongside the much

lower-frequency data on hedge fund returns, in order to

understand the relationships between them. “We end up

with the best possible explanation of how their returns vary

with general market movements, and with other factors like

liquidity, volatility and the availability of leverage,” Ramadorai

explains. Testing the method’s predictions on the few funds

that do provide daily updates on their activities seems to

confirm its accuracy.

What do the results tell us? One clear conclusion is that when

the market becomes volatile, hedge funds generally retreat

from risky assets and switch into safer ones such as short-

maturity bonds – just like most other investors.

This casts some doubt on their claim to be providing vital

liquidity in a crisis, buying when others are fleeing for safety. It

also suggests that those who invest in hedge funds to diversify

their portfolio’s risks could get a surprise if real trouble appears.

Another surprising discovery is that while analysis based

on monthly data might suggest hedge funds have low risk

exposures, higher-frequency analysis shows that at other times

they are exposed to much greater risk. Broadly, they seem to

run the most risk right after they have reported, and the lowest

just before doing so.

More generally, Ramadorai is sceptical of most hedge funds’ claims

to be able to beat the market consistently. Some can, but only a

minority, and only by taking clear risks, he argues. These funds are

quickly spotted by smart investors and deluged with money; early

success often proves hard to replicate at larger scales.

That success comes at a price, too; fees are steep, and have

continued to rise in the last decade. When this is taken into

account, returns don’t look as impressive. “Our research shows

that hedge fund families that have done well in the past do

seem to be able to charge higher fees,” Ramadorai notes. “But

is their performance really better? In general there seems to

be no real difference, after fees, between the best and worst-

performing funds.”

There’s an argument that hedge funds need closer supervision. If

they had to report what they’re doing more often and in more

detail, it would be easier for regulators to ensure they’re not

creating build-ups of risk that could threaten the whole system.

It’s a delicate balance; too much oversight could threaten the

agility that’s among hedge funds’ main strengths. Too little could

mean the next one to implode takes its bank counterparties

down with it, or imperils a major pension scheme.

Ramadorai argues the problem is being addressed, at least

partly. “There’s certainly a case for better reporting, but I think

this is beginning to happen already – hedge funds are gradually

being drawn into the regulatory net,” he says.

All this research is being put to good use. Earlier this year

Ramadorai joined the newly-formed European Securities and

Markets Authority (ESMA), becoming a member of the Group

of Economic Advisors of its Committee for Economic and

Markets Analysis.

Do regulators need to take further steps to stop the spread of crisis? Are there new indicators they should be monitoring for signs of trouble? It’s still early days for ESMA, but before too long the questions Ramadorai and his colleagues discuss at its biannual meetings could become big news for the whole industry.

3

TA R U n R A m A d o R A i

FEATURE

k E v i n S h E P PA R d

When journalists and commentators say

markets are ‘volatile’, they don’t mean it in a

good way. Technically, of course, when a price

rockets upwards it’s being just as volatile

as when it crashes, but you’d never know it

from reading the financial press. Could this

intuitive feeling that volatility is in itself a

bad thing point to a deeper insight? Kevin

Sheppard thinks so.

He’s a financial econometrician, interested in measuring how

financial markets behave at ever-greater levels of detail. That

involves painstaking attention to tick-by-tick financial data,

as well as plenty of computational firepower. His office at the

Oxford-Man Institute (OMI) hosts numerous humming servers

and hard drive arrays – a necessity if you want to tangle with

datasets describing tens of millions of trades a day.

“You hear a lot about volatility in the news, and it’s always a bad thing,” Sheppard explains. “People say the markets are volatile when they’ve fallen. They blame volatility when they lose, but they don’t give it any credit when they win. I did

some research on this, and I couldn’t find a single positive use

of the term in the financial press.

From a statistical point of view, it’s 50-50 – volatility could mean something is rising or falling, but I wondered if there’s more truth to the idea that volatility is associated more with negative returns – if volatility really could be bad for society as a whole.”

He set about finding out by analysing high-frequency data to

extract a different metric, called ‘realised semivariance’, which

is like volatility but also includes information on the direction

prices are moving. Sheppard and fellow OMI researcher Andrew

Patton calculated it in a variety of different markets using prices

sampled every five minutes.

They were particularly interested in sudden, discontinuous

movements, known as jumps, in an asset’s price, which

generally happen when unexpected new information becomes

available – for example, when a central bank changes interest

rates. By definition, such a price jump forms a spike in volatility.

The researchers looked at any five-minute periods where a price

rose or fell significantly relative to the five-minutes immediately

before or afterwards.

This allowed them to decompose volatility into two components

– the everyday volatility that forms a constant backdrop to

all market activity, and the ‘jump volatility’ that’s created by

unexpected news. The use of realised semivariance provided

further guidance by separating out good jumps, where the price

heads upwards because, say, a share buyback is announced, and

bad ones, where long-term investors become worse off.

“We were trying to understand how the market processes these events, when there’s a major positive or negative surprise, and what the implications are for future volatility,” Sheppard explains.

The results were eye-opening. “We found there’s a huge

asymmetry between how good and bad surprises are received.

The impact of good news is transitory, usually lasting a few

days at most, whereas the effects of bad news lasts up to three

months. So when people consistently associate volatility with

bad consequences, they’re right!”

In fact, after a few initial flickers, good news actually seems to

reduce volatility. Investors are happy; not only have they made

money from the good news, but their portfolio’s returns are

smoother for the next few days. Bad news, by contrast,

tends to leave sustained turbulence in its wake. This

kind of asymmetry only appears when looking at

sudden jumps in the market; smaller, steadier price

movements have similar effects whether they are

up or down.

good volatility, bad volatility

4

Until recently, such phenomena were barely visible, because

most people focused on daily price data. It’s only by zooming

in to watch how the market reacts to information over much

shorter periods that researchers can start to understand its

behaviour. “Working with high-frequency data is like having a

microscope,” Sheppard says. “It lets us see what’s going on in

the market in a level of detail that was once impossible.”

He has built on this work by creating sophisticated models that

use this ‘realized’ data to forecast volatility – more accurately

than previous models, over many time horizons, and in asset

classes ranging from US equities to currencies and emerging-

market stocks. The financial industry is slowly adopting such

models, which should help traders and investors manage their

risks more effectively. As assets’ liquidity improves, the models’

range of possible applications will grow still further.

High-frequency econometrics has been growing steadily in

popularity since the mid-1990s. In part this has been made

possible by the widespread availability of rich and detailed

financial datasets. Vastly increased trading volumes on

exchanges all over the world also help. More markets now have

enough liquidity for effects like these to be measurable – it’s

hard to get a detailed idea of a stock’s volatility if it trades just

a few dozen times a day.

Sheppard cites a day during the recent financial crisis when the

S&P 500 index rose by about 10%, then crashed 15% before

rallying in the last hour to close almost unchanged. Examining

only daily price data misses the dramatic swings in the price,

and a risk manager could come away with the impression it was

an uneventful day’s trading.

Likewise, as we emerged from the most turbulent period of the

recent crisis, volatility fell significantly. But someone using only

daily data wouldn’t have noticed until much later. Due to the

noise that using this data introduces into analysts’ calculations,

to get a statistically meaningful result it’s necessary to look back

over a much longer period. This in turn means it takes much

longer to spot emerging trends.

Using more frequent data means analysts can look at two or three days’ worth of prices, rather than a couple of months. In early September 2008, their view wouldn’t have been clouded by the lingering presence of August’s (relatively low) volatility; they would have seen what was going on in the present far more clearly and been able to act promptly as volatility changed.

These findings could be important to risk managers at banks,

hedge funds and other financial institutions. For example, they

could give traders in the options market, who are essentially

betting on changes in volatility, a more up-to-date idea of the

dangers they face.

More specifically, they could have helped financial institutions

assess their risks more accurately in the run-up to, and the wake

of, the crisis. It’s possible some could have weathered it better

as a consequence. One investment bank claimed afterwards

that for two days in a row during the crisis, market movements

were so extreme that they qualified as ‘six-sigma events’. By

definition, these events, when prices move more than six times

as violently as usual, practically never happen. To have two in a

row is almost inconceivable.

The bank was trying to claim it couldn’t possibly have foreseen the events that lost it so much money – they were one-in-a-trillion freak events. But is this true, or was it just looking at the wrong things?

Sheppard suspects the latter. “If you look at how volatility

was changing, those events don’t look all that surprising at

all,” he says. The bank only believed they were so incredibly

unlikely because it didn’t understand how volatility was rising,

which was due to the fact that the data its risk managers were

using wasn’t frequent enough and hid new developments in a

dangerous way.

“In normal times, a 10% drop in the market is very rare,” Sheppard comments. “But in the middle of a crisis it’s not rare at all.” If his work helps drive home that message, he’ll have done us all a favour.

5

k E v i n S h E P PA R d

FEATURE

b E n h A m b ly

Commodities are among the most fertile

fields for innovation in finance today, and Ben

Hambly is helping to create the mathematical

framework that makes it all possible.

Hambly, a Professor of Mathematics and associate faculty

member at the Oxford-Man Institute (OMI), is a specialist in

the mathematics of probability. Throughout his career he has

sought out new and exciting financial markets in need of

firmer theoretical underpinnings. The burgeoning trade in

commodities and the exotic derivatives that are springing up as

a result are the latest area to attract his interest.

Once thought of as stolid and uninteresting compared to more

fashionable assets like equities and credit, commodities have

seen an upsurge in appeal in recent years. New participants

have poured into markets such as metals, oil and electricity, and

interest is increasing at a pace.

“Things are developing incredibly fast,” Hambly says. “Already there are lots of fascinating problems to study, and areas where we don’t yet have a good mathematical model of what’s going on.”

One reason for the peak in interest is the enormous volatility

that commodity markets offer and the opportunities they create

for agile traders. A five percent price move over the course of

a day is considered violent and exceptional in stocks, bonds or

foreign exchange, but it’s nothing in commodities. Wholesale

electricity prices can spike tenfold over just a few hours.

This is partly because what’s being traded isn’t just a financial

instrument, but something that’s needed in the real world. If

someone doesn’t get the shares they want to buy immediately,

it’s no catastrophe. But if the wholesale power market fails to

connect buyers with sellers, homes go dark and factories shut

down. Likewise, if the price of government bonds spike, people

might simply buy less, but if a natural disaster causes an oil

refinery to shut down, prices will rocket.

Electricity is especially volatile because it can’t readily be stored.

At any given time, the market has to find a price at which all

the power being produced can be used.

Banks are desperately trying to create bespoke products that let

traders profit from violent price swings, or power producers and

users manage the risks they bring. As a result financial engineering

in commodity markets is growing ever more complex.

Hambly is working on the mathematics behind these exotic

financial structures. For example, he and colleagues have

created a robust mathematical framework for pricing ‘swing

options’, which give power consumers some rights to buy

electricity at a fixed price - allowing them to hedge the risk of

sudden brief increases in their running costs, but also letting

them benefit if prices fall sharply.

Other innovations to which his methods apply include ‘tolling

agreements’ – exotic derivatives that effectively let market

participants set up as virtual power stations, turning production

on and off as conditions change and looking to profit from the

difference between fuel inputs and power output.

It’s not just the financial industry that’s driving the market’s growth; the industries that produce and consume commodities are moving in too. Energy generators are realising they need mathematical finance techniques if they’re to accurately value the power stations they own, or efficiently finance the construction of new ones.

Likewise, mining and energy extraction firms are realising

that the methods they use to value the reserves that they

plan to exploit in the future are crude, and that using more

sophisticated techniques could transform how they do business.

Hambly is giving them the tools to rethink the energy market.

the mathematics of power

6

One unsettling feature of working on the cutting edge is

that today’s hot new market can turn into tomorrow’s pariah.

Hambly’s previous research includes considerable work on

how to price complex structured credit products, whose value

depends on a large portfolio of underlying assets.

Structures like collateralised debt obligations (CDOs) and

mortgage-backed securities enjoyed explosive growth for a few

years, but plenty of investors lost out when the market crashed

in 2008 and now few new deals are done.

Hambly built a dynamic model that greatly improved on the methods being used to value these deals at the time. It used stochastic partial differential equations to understand the losses that could be expected when buying them. Unfortunately, this radical improvement arrived just when the bottom fell out of the market.

He is resigned, though. “Obviously this wasn’t quite what I

was expecting, but it’s a risk you take when you work in these

areas. In retrospect, the market became far too dependent

on simplistic models, and too divorced from the assets behind

these structures. But fundamentally default risk is still there and

in theory CDOs were a sensible way of sharing it out.”

OMI provides a supportive environment for those working at the

frontiers of knowledge. It brings together people from different

backgrounds, including academics from a variety of fields as

well as finance professionals. A recent OMI seminar on energy

markets, for example, provided Hambly with the opportunity

to exchange insights with fellow academics and senior quants,

as well as traders from investment banks and hedge funds. For

Hambly it was a chance to compare models and pricing methods,

and discuss the trends that are driving the market’s development

feeds into new and interesting financial phenomena.

A major issue of concern to practitioners is the increase in the level of unhedgeable risk in the markets. “Everyone wants to manage risk, but lots of these risks are impossible to hedge,” he comments. “If you’re in the oil market, how do you hedge a political risk like the Arab spring? How could traders in natural gas have protected themselves from the technological advances in shale gas production that have sent prices diving? A lot of this is hard to deal with mathematically,” he admits. “But it’s still vital to know about it if you’re interested in these markets.”

He still works on a broad repertoire of research interests

outside finance. One long-running project draws on the

theory of fractals – mathematical constructs like the famous

Mandelbrot set, which display complex structures at all scales so

that that no matter how far you zoom in, intricate new forms

always become apparent. It turns out these are a great way to

understand how particles move through different kinds of soil.

That said, mathematical finance takes up a great deal of

Hambly’s time. His success in the field might seem a little

surprising, given that he fell into it almost by accident. In the

mid-1990s, when it was an emerging and little-studied field,

Hambly was a young lecturer at the University of Edinburgh.

Senior managers decided to position the university to take

advantage of the fast-growing field and launched a new MSc

course, which Hambly was invited to run.

Back then it was unusual for a PhD student to decide to go into

the financial industry; these days it’s become the default career

choice for mathematicians with a doctorate who don’t want to

remain in academia.

“I had to learn a lot, very quickly!” Hambly recalls. “But it wasn’t long before I realised that many of the problems in mathematical finance were extremely interesting. It’s the combination of challenging mathematics and potential financial impact that makes this such an exciting area to be in.”

7

b E n h A m b ly

FEATURE

mike gilesis 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 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.

More recently, he has developed the multilevel Monte Carlo

method for the pricing of financial options, and is active in

the exploitation of GPUs (graphical processing units) for high

performance computing in a variety of application areas.

georg gottlobis 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.

greg gyurkojoined OMI in 2007 as one of the first student members of the Institute. He obtained a DPhil at the University of Oxford and is currently a Departmental Lecturer in the Mathematical Institute, where he is a member of the Mathematical and Computational Finance Group.

Greg is the course director of the MSc in Mathematical and

Computational Finance, and is actively involved in organising the

Practitioner Lecture series and the Mathematical Finance Internal

Seminar series. Greg’s research interests relate to the theory and

applications of Rough Paths Theory, as well as the development

and software implementation of probabilistic numerical methods

for approximating solutions to stochastic differential equations

and certain types of partial differential equations.

ben hamblyis 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.

mike

greg

geo

rg

m E m b E R S

8

ben

vicky hendersonis 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.

chris holmesmoved 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.

sam howisonis 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.

hanqing jincompleted his PhD in Financial Engineering in 2004 at the Chinese University of Hong Kong. He is a University Lecturer at the Mathematical Institute, is on the editorial board of Mathematical Methods of Operations Research and is also a member of the Mathematical and Computational Finance Group.

His research interests include portfolio selection, behavioural

finance, applied stochastic analysis and optimisation. He has

previously worked on stochastic control, portfolio selection

with transaction costs and behavioural portfolio selection. He is

currently working on time consistency of dynamic decisions.

m E m b E R S

vicky chri

s

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han

qin

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PEOPLE

sam

shin kanayais 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 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 working on the following projects: nonparametric

testing of the stationarity for continuous-time Markov

processes, and nonparametric estimation for mixed frequency

time series data.

gechun liangjoined the Oxford-Man Institute as a Postdoctoral Research Fellow in the Michaelmas Term of 2010. Prior to that, he was a student member of the Institute whilst completing a DPhil in Mathematics at the Mathematical Institute under the supervision of Professor Terry Lyons. He has a Master’s Degree in Mathematics from Tongji University, and studied finance as an undergraduate in Jilin University.

His research interests are mainly focused on mathematical

finance and applied probability. He is especially interested

in backward stochastic differential equations and credit risk

modelling.

terry lyonsis the newly appointed Research Director of the Oxford-Man Institute. He is the Wallis Professor of Mathematics at the University of Oxford, a Fellow of the Royal Society and one of the UK’s leading mathematicians, having made a number of contributions to stochastic analysis.

His interest in stochastic analysis relates particularly to the

control of non-linear systems driven by rough paths. Prime

examples of such systems are provided by stochastic differential

equations and stochastic systems.

His research on ‘rough paths’ has founded a new field,

stimulating an enormous amount of work, allowing

breakthroughs in many areas such as numerical analysis. He has

a deep understanding of the role of risk in financial markets

where he is known for his work on managing uncertainty in

volatility, and for developing cubature methods as new tools

allowing more efficient numerical modelling.

josé martinezis 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.

gechun

josé

terr

y

shin

m E m b E R S

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sergey nadtochiyis 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.

jan obłójis 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,

comparative performance of robust and classical hedging

methods, portfolio optimisation under pathwise constraints and

hedge-funds managers’ incentive schemes, inverse problems for

utility maximisation.

han ozsoylevis a Lecturer in Financial Economics at the Saïd Business School. Before joining the University of Oxford, he earned his PhD in economics from the University of Minnesota and BSc in Mathematics from Bilkent University. He has held visiting appointments at the University of California - Berkeley and Johns Hopkins University.

Han’s research primarily focuses on financial market

imperfections, such as those generated by asymmetric

information, imperfect competition, behavioural biases, and

bounded memory. He has studied information sharing amongst

stock market investors and, in particular, how social and

information networks affect asset prices and investor welfare.

He is also interested in questions related to financial fragility,

liquidity and market manipulation.

tarun ramadoraiis 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

m E m b E R S

taru

n

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PEOPLE

sergey

han

steve robertsis 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.

neil shephardis Head of Financial Econometrics and Statistics at the Oxford-Man Institute and a 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.

kevin sheppardis 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 completed his PhD at the University of California, San Diego.

ruediger stuckeis 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.

mungo wilsonis 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.

mu

ngo

kevin

rued

iger

neil

m E m b E R S

steve

12

thaleia zariphopoulouis 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.

lan zhangis a Reader in Finance. Her research focuses on market microstructure, statistical arbitrage and high frequency financial econometrics. She has developed a number of influential methods for analysing high frequency financial data, including the two-scale and multi-scale realised volatility estimators (TSRV, MSRV) to handle market microstructure.

Recently Lan analysed the general theoretical properties of

local constancy approximation in continuous semimartingales.

Her current work includes the analysis of limit order books

observed in real time, robust estimation of high frequency

quantities and its application to portfolio management and

options trading.

Lan has published widely in leading journals including

Econometrica, Review of Financial Studies, Journal of

Econometrics, Journal of American Statistical Association,

Bernoulli, and Annals of Statistics. She is an Associate Editor

of the following academic journals: Statistics and Its Interface,

Annals of Applied Statistics, and Econometric Theory. She is

on the advisory board of the International Center for Futures

and Derivatives at the University of Illinois at Chicago. She

completed her undergraduate degree at Peking University in

China and obtained her Master’s and PhD degree from the

University of Chicago.

xunyu zhouis 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.

m E m b E R S

13

PEOPLE

thaleia

xun

yu

lan

bahman angoshtariis a second 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.

sylvestre burgos is studying for a DPhil in Mathematics within the Mathematical and Computational Finance Group. He holds a BSc in Mathematics from the University Paris VI, an MSc in Engineering from the Ecole Centrale Paris and an MSc in Mathematical and Computational Finance from the University of Oxford.

Sylvestre’s research interests lie broadly in the field of numerical

methods for computational finance. His research under the

supervision of Mike Giles focuses on the computation of Greeks

with Multilevel Monte Carlo simulations.

vladimir cherny is a second year DPhil student at the Mathematical Institute. His research interests lie broadly in stochastic analysis and optimisation theory with their applications to mathematical finance.

He is working under the supervision of Jan Obłój on

implementing methodology of Azema-Yor processes for

different optimisation problems in mathematical finance, such

as long-term expected utility growth rate maximisation subject

to drawdown constraint.

martin gould is a second year DPhil student in Mathematics. He holds an MASt (Part III) in Mathematics from the University of Cambridge and a BSc in Mathematics from the University of Warwick.

His primary research interest is the limit order book, and in

particular in developing a dynamic stochastic model of limit

order trading that is better able to explain the diffusive nature

of the return series in foreign exchange markets. He hopes to

be able to extend his model to gain insight into how prices

are affected by the release of macroeconomic news by central

governments and to examine how changes in limit order arrival

flows propagate through the network of different currency pairs.

ni hao is a second year DPhil student in the Stochastic Analysis Group. Ni previously completed a Bachelor’s Degree in Mathematics at Southeast University, China and a Master’s Degree in mathematical and computational finance at the University of Oxford.

She is currently working on rough paths theory with her

supervisor Professor Terry Lyons, and her research interest is the

expected signature of stochastic processes.

S T U d E n T S

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vladimir

martin

ni

sylvestre

you

ness

bah

man

aren

d

richard hillsis a DPhil student in Financial Economics from the Saïd Business School, and is interested in the effect of the factors determining liquidity in financial markets. His current research is on liquidity as measured by price impact in market microstructure models (as opposed to transaction costs or bid-ask spreads), and investigating models where this price impact is random, and hence there is liquidity risk.

He has previously worked for various technology companies,

and spent two years in Credit Derivatives Technology at Morgan

Stanley. He has a MEng in Engineering from the University of

Oxford, and an MPhil in Finance from Cambridge.

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.

sigrid källbladis a second year DPhil student in the Mathematical and Computational Finance Group. Sigrid works under the supervision of Professor Thaleia Zariphopoulou and her research interests are in stochastic control and portfolio optimisation.

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. Her research interests include time series forecasting, spatiotemporal correlation modelling and latent Gaussian processes.

Ada 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. She has submitted her thesis

on “Probabilistic Wind Power Forecasts: From Aggregated

Approach to Spatiotemporal Models”.

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.

S T U d E n T S

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PEOPLE

ni

anth

on

y

ada

nath

aniel

sigrid

richard

arnaud lionnetis a DPhil student at the University of Oxford. His interests in mathematics include functional analysis and probability theory and he is very interested in complex and dynamical systems, especially when they involve randomness (markets, population evolution, meteorology, etc).

More specifically he is interested in stochastic differential

equations, Malliavin’s calculus and rough paths. He specialises

in backward stochastic differential equations, which he

finds interesting for two of their fields of application: their

connections with some kinds of partial differential equations on

the one hand and some problems of mathematical finance on

the other (option pricing, risk measures).

kasper lund-jensen is a DPhil student in Economics at Nuffield College. Prior to his doctoral studies he completed a BSc in Economics at the University of Copenhagen and a MSc in Finance and Economics at the London School of Economics.

Kasper’s research interests lie in the areas of financial

econometrics and economic forecasting. Currently, his research

is focused on out-of-sample equity premium predictability and

combination forecasts.

diaa noureldin is a DPhil student in Economics. He is interested in financial econometrics, particularly modelling and forecasting volatility and dependence in financial time series.

He is interested in developing methods suitable for large

dimensional systems and high-frequency data. Diaa previously

studied for an MPhil in Economics at the University of Oxford,

and holds a BA and MA in Economics from the American

University in Cairo. In Michaelmas 2011, he will join the

Department of Economics at the University of Oxford as a

Postdoctoral Research Fellow.

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.

daniel schwarzDaniel Schwarz is a DPhil Student at the Mathematical Institute and a member of the Mathematical and Computational Finance Group. Previously he obtained a Master of Mathematics (MMath) degree from the University of Oxford.

His current research is focused on the stochastic modelling of

energy markets. In particular he has been developing models

for spot and derivative prices in carbon emission and electricity

markets and worked on the pricing of spark and dark spread

options, which are routinely used to value power plants. In

addition, Daniel is interested in the asymptotic analysis of these

models, which provides intuition for the underlying dynamics

and leads to approximations that are useful for the calibration

to market data.

michael streatfieldis interested in hedge funds and investment management. He is a third year doctorate student supervised by Tarun Ramadorai. In his research work he has been analysing the determinants of hedge fund management and incentive fees and in particular exploring how hedge fund management companies set prices for the future funds they launch.

His future research involves analysing the impact of the recent

crisis on hedge fund reporting. Prior to his DPhil, Michael

worked in the investment industry for

15 years in London and South Africa.

S T U d E n T S

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diaa

daniel

mic

hael

cavit

kaspar

arn

aud

How did your involvement in the Institute first come about?

I was already part of an existing interdisciplinary network in financial research at the University

of Oxford when Man Group approached us in 2006 about the opportunity to create the Institute.

I lead the team responsible for putting together the University’s pitch. Our vision for the Institute

was very closely aligned to Man’s aspirations and when the agreement was formalised, the

University invited me to be the Institute’s first Director, which I was very happy to accept.

What was your vision for the Institute when you first opened its doors in September 2007?

Our aims for the Institute were very clear when it was first founded and we continue to strive

to achieve them. We’re here to be excellent at academic work in quantitative aspects of

finance. We want to generate new ideas that are applicable to financial problems, to write

good papers and be respected by our peers around the world for what we are doing. We’re

also dedicated to training the next generation of researchers and attracting the highest calibre

of young people to come and work here.

Did you have any personal objectives when you took on the role of Director of

the Institute?

I wanted it to be more than just a space where people can focus and develop their academic

ideas. I wanted it to increase their aspirations. People can often be very self-limiting. They’ll

have ideas, but feel constrained about what they can actually achieve. I wanted the Institute

to encourage people to raise their game and to overcome barriers – to seek out opportunities.

My aim was to create an exciting place that people would want to come to.

Do you feel you’ve achieved that aim?

Absolutely - I think the strongest indicator of the Institute’s success is the calibre

of people we’re attracting to the UK from around the world. We have an

extraordinary group of post doc researchers here. They’re second to none

in the world and I’m so proud that they’ve chosen to spend the most

crucial time in their career here. Our newest recruit who joins us in

September 2011 had something like 14 offers from some of the best

universities around the globe and he’s chosen to come here. It’s a

very real indicator to the world that we’re a centre of excellence!

In July this year Neil Shephard stepped down as

Director of the Oxford-Man Institute (OMI). Neil has

played an integral role in the formation and continued

development of OMI. We spoke to him to gain a closer

insight into his aspirations for the Institute and the

reasons behind his decision to step down as Director.

OMI bids ‘au revo ir ’ to its founding Director

2

Part of your initial vision was to generate new ideas in the area of quantitative

finance – do you feel you’ve achieved that objective over the last four years?

Some very good ideas have been generated at the Institute. Two of note include Mike

Giles’ work on harnessing graphics cards to complete calculations quickly using simulated

complicated derivatives, and the software my colleague Tarun Ramadorai’s been developing

to provide a unified view of hedge fund return databases. They’re extremely exciting and

receiving a lot of recognition.

What has the Institute enabled you to achieve?

OMI has enabled me to take my work to the next stage of empirical relevance. I could have

completed my research in a more abstract, theoretical way, but it has enabled me to take it to

a more applied situation. To some degree it’s because I’ve had the opportunity to talk to Man’s

commercial team, but it’s also down to the fact that we have better data resources here, a very

good computational infrastructure with fantastic compute servers and specialist graphics card

machines, better funding and an extremely efficient administration team that I’m very proud

of. They do a great job, which leaves us to focus on our research!

What has prompted your decision to step down as Director?

My family and I are relocating to London and I feel very strongly that the Institute’s Director

should be here full-time, which will become impossible for me based in London. I also feel that

it’s time for OMI to move on and for some fresh ideas to be injected into it. I hope to be able

to contribute to the Institute’s successes in the future, but it’s time for someone else to step up

and to take it to the next level.

What would you like to see the Institute achieve in the future?

I’d like to see it go from strength to strength: to continue on its path to becoming recognised

as the world’s centre of excellence in our academic field, which I think we’re well on the way to

achieving. I’d like to see more effective use of our interdisciplinary strengths and more research

projects across disciplines, which I know my successor is keen to pursue. I’m extremely proud of

what has been achieved, but there’s a lot more that can be done and I’m happy to be stepping

down to let someone else take up the mantle.

What was the reasoning behind the collaboration

with the University of Oxford?

[Tim Wong] In 2006, we decided that we wanted to create a

collaboration with a leading research institution in the UK

or Europe to help us develop the business. We had a vision

of bringing together the commercial and academic worlds of

quantitative finance as, in our experience, these two worlds

never seemed to meet in a meaningful way. After discussions

with several top academic institutions it was clear that Oxford

was the natural choice. They had an appetite to do it, they

saw opportunity, and they had a unique expertise in the

departments and disciplines we were interested in.

Why is this collaboration so innovative?

[TW] The closeness of the academic and commercial

relationship is unique – it’s something that a lot of people

have talked about creating, but few have realised.

[Anthony Ledford] You have leading academics and leading

commercial practitioners working together on a daily

basis. It is also a multi-disciplinary environment attracting

people from different subject areas including econometrics,

mathematics, statistics, computer science and engineering.

By drawing on expertise from related fields, you bring new,

creative ideas to both practical and theoretical problems.

What have been the key commercial benefits to Man?

[AL] Man has been able to develop part of its tail protect

strategy using models that were first developed and openly

published by academics in the Oxford-Man Institute (OMI).

The initial research was focused on building a forecasting

model, but we saw huge potential in this and incorporated it

into something that was a marketable trading product. That

occurred because we have a network of quants within Man

who meet up regularly in Oxford and gain exposure to the

work of the academics there.

[TW] We’ve also brought both expertise and people from

Oxford into our electronic trading division where most of

the underlying machinery relies on market micro-structure

methods – a field of practice where Oxford has much to offer.

Has the OMI achieved what you’d hoped it would?

[TW] Yes, it has. The last four years have represented only the

first phase of the collaboration and I think we have in many

ways exceeded our objectives by achieving an ‘openness’ with

the academics.

[AL] They have really embraced this, and we are delighted by

the people we’ve come across, and also by the fact that we

have been able to recruit key talent into our business as well.

Will there be a change of direction with the

appointment of Professor Terry Lyons as the new

OMI Director?

[AL] Neil was instrumental in helping us develop the right

model of collaboration between the University and Man, and

also in persuading academics from various departments to

make this multi-disciplinary institute a reality. There will be

some change of emphasis as Neil comes from an econometrics

background and Terry comes from a mathematics background,

but the core principles of the Institute will remain the same.

[TW] The aim now is to build on this foundation and establish

OMI as the world’s leading quantitative finance institute

and also to see whether we can derive more fruits for Man

through this collaboration.

[AL] In the long run, I’d like to see the Institute’s research

encompass three things: research which is academically world-

leading, research which has wide market and industry systemic

benefit, and research which will benefit Man specifically. OMI

is established in the first area and now it’s about building our

ideas in the other two over the next period.

What are the plans for the future?

[TW] We recently presented back to the management board

to discuss funding for OMI and we are pleased to announce

that this has been renewed until 2015. We’re looking for

opportunities to expand in Oxford in ways that benefit the

wider Group. Work has been very AHL-focused up until

now, but we’d like to have representation from our other

businesses – GLG, Multi-Manager, and MSS.

Man Group ’s co l l id ing wor ldsTim Wong, CEO, AHL and Anthony Ledford, Chief Scientist, AHL discuss their vision of bringing together the commercial and academic worlds of quantitative finance, and choosing Oxford as the place for it all to happen.

4

You must be delighted to have been offered the Directorship

having been involved in OMI since its inception?

The last four years have been a very exciting time for us all. There’s been

a fantastic scale of achievement under Neil’s guidance and leadership.

From our initial work in putting together the bid, we’ve become a

substantial research institute, which independent assessment confirms

has a very wide and respected international reputation as a leading

research institute in quantitative finance.

It certainly seems to attract a very international group of academics

from varied disciplines, is that something you want to build on?

The Institute’s main objective is to address the key problems associated

with financial markets and risk in a way that has significant impact, and

I believe this requires a truly multi-disciplinary effort. It also requires

world-class researchers, which, thanks to the funding we receive from

Man, we are able to attract and recruit. The problems are often too

complex for a single discipline to resolve, but we are fortunate that my

predecessor has drawn together a wide range of individuals with the

right variety of expertise to identify and work on joint projects.

There are already a number of collaborative projects that have

been undertaken at OMI, is this something you’re keen to pursue?

One of my main goals is to create a framework that encourages and

enables collaborations to happen, which is a challenge. There is an

intrinsic contradiction between multi-disciplinary work and disciplinary

excellence. To really succeed in multi-disciplinary projects you need

people who are absolute masters of their field. But there’s a tension

between people’s need to work on their own and contributing to

broader goals. I don’t underestimate the challenge, but it’s absolutely key

to really innovative research, so it’s essential that we make the effort and

succeed in this objective.

In his first interview since his appointment as Director of the Oxford-Man Institute (OMI), Professor Terry Lyons discusses his goals for building on the Institute’s suc cessful foundation.

A multi-discipl inary challenge for the new Director

6

In his first interview since his appointment as Director of the Oxford-Man Institute (OMI), Professor Terry Lyons discusses his goals for building on the Institute’s suc cessful foundation.

A multi-discipl inary challenge for the new Director

Isn’t there a concern that this will detract from a member’s

individual research?

It’s very important that the early stage researchers have an

opportunity to build a strong disciplinary foundation. They should

not have to worry about having to prove their work as applied

to anything. I want to nurture students in their disciplines, but

let them benefit from being part of a multi-disciplinary team.

However, I see no reason why, as researchers develop - and we’re

fortunate enough to have a number of world-class researchers

across the spectrum at OMI - that we cannot identify projects

where expertise in different disciplines can be brought together to

create something that is absolutely cutting edge and I expect my

colleagues to want to jump at those opportunities.

How do you intend to achieve this multi-disciplinary focus?

The ingredients for an environment that will nurture

collaborations are rather intangible. A good collaboration can be

triggered through a casual conversation, so I think it’s important

to enable conversations to transpire on a large scale. The OMI

environment already has many aspects that are effective at

achieving this, such as our common dining area, our large number

of graduate and post doc students and faculty, along with joint

seminars, and I’d certainly like to see these develop.

I’d also like to see members get together and articulate current

projects that are already achieving something quite special, but

could benefit from involvement from other disciplines. We then

need to make sure that we use our resources to facilitate these

projects in moving forward. Through our collaboration with Man

we have tremendous resources available to us, which we should

capitalise on to ease those early stages and ensure we get quality

output. A lot of it is about making sure there are no impediments

to getting started.

OMI enjoys a unique relationship with Man Group. Aside

from the funding they provide, how does the relationship

benefit members of OMI?

To do quality research in an area such as finance, really does

require a detailed engagement between practitioners and

academics. We’re extraordinarily fortunate to have this

collaboration with Man. Sharing a physical environment is hugely

beneficial. We have lunch together and they attend our seminars:

we exchange scepticisms and use their insight to sharpen and

focus our thinking. It’s a very rewarding resource that shapes and

refines the quality of our research. They’ve also benefitted as they

have taken on a number of technicalities that we have developed

and used them in their own research.

What other aspirations do you have for the Institute as you

step into the role of Director?

I would very much like to see jointly funded research projects

with the industrial community. I also believe we should use our

strengths to get involved in projects that have a public interest,

such as effective and novel ways to understand and measure

the risk involved in positions held by banks and other financial

intermediaries – in which case we could leverage our core funding

to add considerable value to any government/research council

funded projects we undertake.

We’d like OMI to be seen as a portal for the whole of the UK’s

academic research in the area of quantitative finance. We

welcome the engagement of our colleagues and practitioners in

the UK and we’re already moving towards closer engagement with

them by taking advantage of Man Group’s fantastic new offices

and lecture theatre in London.

It’s an exciting time and I’m very pleased to be involved. It’s hard

to imagine that in just four years we could have moved so far and

engaged the quality of people and projects that are here at OMI.

Now it’s time to build on that solid foundation.

nithum thain is a MRes student in Computer Science. He holds a Bachelor’s in Mathematics from Queen’s University and a Master’s in Mathematics from McGill.

Nithum’s research interest is in algorithmic game theory,

particularly in financial and economic models that apply game

theoretic structures to practical phenomena. For his current

research, he is considering multi-agent coordination strategies.

kaiwei wangis a first year DPhil student in the Mathematical and Computational Finance Group at the Mathematical Institute. His research is focused on behavioural finance and time inconsistent problems.

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 focuses on numerical methods in finance, and he is currently working on a Multilevel Monte Carlo method for jump processes. He is also interested in other topics in financial mathematics, such as volatility modelling.

weijun xuis a DPhil student in the Stochastic Analysis Group under the supervision of Terry Lyons at the University of Oxford. Before joining Oxford, he completed a Bachelor’s Degree in Economics and Mathematics at Shanghai Jiaotong University and a Master’s Degree in Statistics at Harvard.

His research interests lie in the area of probability. He is currently

working on the problem of inversion of signature for paths of

bounded variation. Together with Professor Terry Lyons, he has

developed methods to invert the signature for axis paths, which

can only move parallel to the axes. Now he is trying to solve the

inversion problem for general paths of bounded variation.

danyu yangis working with Professor Terry Lyons on rough path theory and its applications. She is interested in extracting nontrivial information of the path from its signature. She is currently working on the potential application of rough path theory to Harmonic analysis, especially to the convergence problem pioneered by the celebrated theorem of Carleson.

yifei zhongis a third year DPhil student in the Mathematical and Computational Finance group of the Mathematical Institute. He is supervised by Xunyu Zhou and Hanqing Jin.

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.

His research is currently focused on optimal stopping time and

applied PDEs. He is also interested in behavioural finance and

time inconsistent problems.

S T U d E n T S

17

PEOPLE

kaiwei yifei

dan

yu

yuan

sum

ud

u

nith

um

weijun

horatio boedihardjo

DPhil Student at the Mathematical Institute, University of Oxford.

Research Interests: Schramm-Loewner Evolution in Riemann

Surfaces

andrea calì

Lecturer, Brunel University.

Research Interests: Knowledge Representation and Reasoning,

Database Theory, Web Information Systems, Information

Integration, Logics and Databases

tom cass

Postdoctoral Research Assistant at the Mathematical Institute,

University of Oxford.

Research Interests: Stochastic Analysis, Probability Theory and

Mathematical Finance

samuel cohen

Junior Research Fellow at St. John’s College.

Research Interests: Stochastic Analysis and Mathematical Finance

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

thomas flury

Quantitative Research Analyst, AHL.

Research Interests: Time-series Econometrics, Financial

Econometrics and Parameter Estimation with Particle Filters

matthias hagmann-von arx

Head of Equities Strategies, AHL.

Research Interests: Non and Semi-parametric Econometrics,

Empirical Finance, Systematic Trading Strategies

tim 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

nick 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

dmitry kramkov

Professor at Carnegie-Mellon University, Pittsburgh and

part time Professor at the University of Oxford.

Research Interests: Computational Finance – Financial

Derivatives, Optimal Investment, Numerical and Software

Implementations of Financial Algorithms

jeremy large

Research Economist, AHL and Fellow of St. Hugh’s College.

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

colin mayer

Professor of Management Studies, Saïd Business School,

University of Oxford.

Research Interests: Corporate Finance, Corporate Governance,

Corporate Taxation, Regulation of Financial Institutions

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

A S S o c i AT E m E m b E R S

18

per mykland

Robert M. Hutchins Distinguished Service Professor,

Department of Statistics, The University of Chicago.

Research Interests: High Frequency Financial Econometrics

thomas noe

Ernest Butten Professor of Management Studies and Fellow of

Balliol College.

Research Interests: The Application of Game Theory to the

Design of Financial Securities and Corporate Governance Systems.

The Interaction Between Product and Financial Markets and the

Effect of Financial Markets on Managerial Incentives.

wei pan

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

Research Interests: Application of Cubature Method to Various

Option Pricing Problems

andrew patton

Associate Professor of Economics, Duke University.

Research Interests: Financial Econometrics, Forecasting,

Volatility and Dependence Models, Hedge Funds

cornelius 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.

zhongmin qian

University Lecturer in the Mathematical Institute and Fellow at

Exeter College.

Research Interests: Rough Path Analysis and Non-linear Partial

Differential Equations

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

torsten schöneborn

Quantitative Analyst, AHL.

Research Interests: Market Microstructure, Optimal Trade

Execution, Optimal Investment under Transaction Costs

bernard 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

suresh sundaresan

Chase Manhattan Bank Professor of Financial Institutions,

Columbia University.

Research Interests: Central Bank Liquidity Provision, Hedge

Funds, Asset Allocation

lukas szpruch

Nomura Research Fellow at the Mathematical and

Computational Finance Group within the Mathematical Institute.

Research Interests: Theoretical and Applied

Probability Theory, Stochastic Analysis and Numerical

Methods for Stochastic Processes

pedro vitori

DPhil Student in Mathematics, University of Oxford.

Research Interests: Stochastic Analysis, Optimal Control and

Mathematical Finance

jan hendrik-witte

DPhil Student at the Mathematical Institute, University of Oxford.

Research Interests: The Development of Unconditionally Stable

Finite Difference Schemes for the Numerical Solution of Non-

linear Partial Differential Equations in Finance

A S S o c i AT E m E m b E R S

19

PEOPLE

Hedge Fund Conference 19th november 2010

Organising Committee

Andrew Patton, duke University and oxford-man institute and Tarun Ramadorai, University of oxford

Speakers

David Hsieh, Duke University; Wei Jiang, Columbia University;

Philippe Jorion, University of California, Irvine; Robert Kosowski,

Tanaka Business School, Imperial College; Tarun Ramadorai,

University of Oxford; Oliver Scaillet, HEC Geneva

Adam Smith Asset Pricing Workshop25th march 2011

Organising Committee

christian Julliard, london School of Economics; Anna Pavlova, london business School; Tarun Ramadorai, University of oxford; Raman Uppal, london business School; mungo wilson, University of oxford; kathy yuan, london School of Economics

Speakers

Anisha Ghosh, Carnegie Mellon University; Christian Julliard,

London School of Economics; Alex P. Taylor, Manchester Business

School; Bryan Kelly, University of Chicago; Seth Pruitt, Federal

Reserve Board of Governors; Snehal Banerjee, Northwestern

University; Jeremy Graveline, University of Minnesota; Jules van

Binsbergen, Northwestern University and Stanford GSB; Michael

Brandt, Duke University; Ralph Koijen, University of Chicago;

Doron Avramov, Hebrew University of Jerusalem and University

of Maryland; Tarun Chordia, Emory University; Gergana Jostova,

George Washington University; Alexander Philipov, George

Mason University; Anders Anderson, Institute for Financial

Research (SIFR); Jose Vicente Martinez, University of Oxford;

Frederico Belo, University of Minnesota; Vito Gala, London

Business School; Jun Li, University of Minnesota

This event was hosted at Saïd Business School with funding

contributed from Oxford-Man Institute.

Advances in Portfolio Theory and Investment Management13th-14th may 2011

Organising Committee

ioannis karatzas, columbia and inTEch; Alex Schied, University of mannheim; Thaleia Zariphopoulou, University of oxford

Stochastic Portfolio Theory

Speakers

Erhan Bayraktar, University of Michigan, Ann Arbor; Robert

Fernholz, INTECH; Kostas Kardaras, Boston University; Vassilios

Papathanakos, INTECH; Johannes Ruf, Columbia University;

Winslow Strong, University of California, Santa Barbara

Portfolio Management under Forward Criteria

Speakers

Nicole El Karoui, École Polytechnique; Marek Musiela, BNP

Paribas; Sergey Nadtochiy, University of Oxford; Michael

Tehranchi, University of Cambridge

Optimal Execution of Trades

Speakers

Aurélien Alfonsi, ENPC; Charles-Albert Lehalle,

Crédit Agricole Cheuvreux; Alex Schied, University

of Mannheim; Sasha Stoikov, Cornell University

E v E n T S

20

The New Commodity Markets14th-15th June 2011

Organising Committee

René carmona, Princeton University and Thaleia Zariphopoulou, University of oxford

Speakers

Knut Kristian Aase, NHH; Fred Espen Benth, University of Oslo;

Álvaro Cartea, Universidad Carlos III; Umut Cetin, LSE; Hélyette

Geman, Birbeck; Ben Hambly, University of Oxford; Sam Howison,

University of Oxford; Vincent Kaminski, Rice University; Rüdiger

Kiesel, University of Duisburg-Essen; Lars Lochstoer, Columbia

University; Ronnie Sircar, Princeton University; Nizar Touzi, École

Polytechnique; Wei Xiong, Princeton University

OMI and OCCAM Joint Workshop on Stochastic Differential Equations: Numerical Algorithms and Applications8th-10th August 2011

Organising Committee

lajos Gergely Gyurko, University of oxford; lukas Szpruch, University of oxford; konstantinos Zygalakis, University of oxford

Speakers

David F. Anderson, University of Wisconsin, Madison; Radek Erban,

University of Oxford; Peter Friz, TU Berlin; Mike Giles, University of

Oxford; Desmond J. Higham, University of Strathclyde; Arnulf Jentzen,

Princeton University; Peter E. Kloeden, Goethe Universitat; Terry Lyons,

University of Oxford; Stéphane Menozzi, Université Paris VII,

Denis Diderot; Christoph Resinger, University of Oxford;

Erik von Schwerin, KAUST; Eric Vanden-Eijnden, NYU

This event was jointly funded with the Oxford

Centre for Collaborative and Applied

Mathematics (OCCAM).

E v E n T S

21

Workshops and Courses

Stochastic Portfolio Theory, 12th may 2011 Ioannis Karatzas, Columbia University and INTECH

An overview of stochastic portfolio analysis building on the work of E.R. Fernholz,

A. Banner, C. Kardara, S. Pal, V. Papathanakos, T. Ichiba, D. Fernholz and J.Ruf.

Risk measures, June 2011 Fred Delbaen, ETH, Zurich

Two sets of two lectures around the topic of risk measures. As a real expert in this field,

these lectures attracted a large audience.

new commodity markets, 13th June 2011 René Carmona, Princeton University

A general introduction to the commodity markets, emphasising the physical nature of the

interests underlying the contracts and derivatives, including discussion of the growing

role of commodity indexes, the impact of the recent regulations, and some of the newest

markets. A second lecture concentrated on specific mathematical models, their analysis and

implementations using the examples of spread options.

lmS-EPSRc Summer School, 18th- 22nd July 2011 Dr Michael Monoyios, University of Oxford

This event was jointly funded with the London Mathematical Society.

EVENTS

S T U d E n T c o l l A b o R AT i o n

22

Student members of OMI are awarded personal desk space in

the Institute, access to its computational resources, a research

allowance of £2000 per annum, as well as admission to its

common room, catering and busy conference and seminar

programme. In return, students are expected to spend around

half their working week at the Institute, but as Korda and Lee

explain, the obligation is no hardship.

“The Institute’s resources are exceptional - far better than what’s currently available through my department. I get great desk space here, a lovely double computer screen, and access to any kind of server I want to use,” says Korda. “Because its run like a business, we’ve got dedicated IT and administration teams that are extremely efficient, which makes life very easy for us.”

Lee adds, “I work in computational statistics, and it can often be very hard to get what you need computationally, but the IT department here is fantastic. If you ever need anything you always know exactly who to go to and they respond very quickly.”

Both students recognise that the funding they’ve received from

the scholarship has played a significant part in advancing their

research. It has not only provided the opportunity for them to

travel to conferences and explore collaborations, but in the case

of Korda, it has enabled him to maintain essential contact with

his supervisor. He explains, “This year my supervisor moved to

Toulouse and without the scholarship funds I wouldn’t have

been able to afford to travel to see him. Having the extra

£2,000 a year gives us more freedom and opportunity in our

research, and the fact that lunch and dinner is provided really

eases the pressure on my personal finances and my time.”

Of course, OMI’s purpose built building was designed to

encourage interaction between the Institute’s academic

researchers and the commercially focused Man Research

Laboratory. For students at the Institute, the opportunity to

gain real industry insight from the Man Group is invaluable.

As Korda explains, interaction is practically unavoidable,

“Sharing common areas and lunch and coffee breaks with the Man Group gives you exposure to people working in the industry that wouldn’t be available to you otherwise. It’s given me a chance to learn about the banking industry and what their work involves.”

The interdisciplinary nature of the Institute has also had a

major impact on both students’ research. The opportunity to

work alongside academics from different research fields within

quantitative finance has not only enhanced their personal

research, but has led to a collaboration between the two

students, that is unlikely to have materialised otherwise.

students members strike up more than just a collaboration…

Students play a significant role in the life of the Institute. Over 20 scholarships are awarded

each year to DPhil students at the University of Oxford who are researching topics connected

with quantitative finance. Nathan Korda and Anthony Lee both completed their DPhils in

the summer and have been members of the Institute for the duration of their doctorates. For

them, the Institute has provided an environment that has nurtured their understanding of

quantitative finance, broadened the scope of their research and afforded many opportunities

that would otherwise have been unavailable to them.

S T U d E n T c o l l A b o R AT i o n

23

“The interdisciplinary nature of the Institute has opened up the opportunity for me to talk to people in different fields. You’d be missing an opportunity if you just allowed yourself to focus on your research in isolation when you’ve got such high calibre people involved in similar fields, all under one roof,” says Lee.

Over the last two years Korda and Lee, a probabalist and

statistician respectively, have been collaborating on a research

project. Sharing an office in the Institute’s original building

three years ago sparked a common interest for a strategy in

the exploitation and exploration trade-off business. As Korda

explains, the opportunity to discuss his research problem

with a statistician is unlikely to have transpired outside of the

Institute, “We would not have achieved this collaboration

without being here. It’s been a slow burning project since

we first shared an office three years ago and it’s certainly

something we’ll continue to work on together when we leave.”

Korda’s personal research project has also been heavily influenced by his interaction with Lee. “My conversations with Anthony made me pay much more attention to the statistical side of my problem. So much so, I’ve decided that I’m going to an applicable statistical institute in Lille to do my post doc in September, as I have realised that it is important for me to focus much more on the statistical side of my research.”

Lee has also benefitted from the opportunity to collaborate

with Korda. He explains, “It’s been really helpful to talk in

depth with someone working in probability. It’s very beneficial

to talk to someone who has a keener attention to mathematical

rigour than I do - at times!”

Through the Institute, the two students have struck up more than just a research collaboration. Their shared love of music has resulted in them forming a jazz band, which has performed at the Institute on more than one occasion. Asked whether the band is open to members of the Institute, Korda jokes, “Some people have suggested that they’d like to sing with us, but they never put themselves forward forcibly enough!”

So, if any members do fancy a turn in front of the microphone,

now’s your chance - the Institute may have helped form a

lifelong academic association, but the band will have to disband

when Korda leaves for Lille and Lee starts his post doc at

Warwick in September 2011.

FEATURE

faculty

Long-term visitors:

Fred Delbaen, Department of Mathematics, ETH, Zurich

Ronnie Sircar, Operations Research & Financial Engineering,

Princeton University

Mingyu Xu, Institute of Applied Mathematics, Academy of

Mathematics and Systems Science, Chinese Academy of Sciences (CN)

Short-term visitors:

Marco Avellaneda, Professor of Mathematics, Courant Institute

of Mathematical Sciences, NYU

Leopoldo Bertossi, School of Computer Science, Carleton University

Francis Caron, University of British Columbia

Robert J Elliot, RBC Financial Group Professor of Finance,

Haskayne School of Business, University of Calgary, Alberta

Ioannis Karatzas, Eugene Higgins Professor of Applied

Probability, Columbia University and INTECH

Marcin Kacperczyk, Assistant Professor of Finance,

Leonard N. Stern School of Business, New York University

Eva-Maria Lütkebohmert-Holtz, Head of the Research Group

for Quantitative Finance Pricing of Risks in Incomplete Markets,

University of Freiburg

Klaus Ritter, Computational Stochastics, Department of

Mathematics, Technische Universität Kaiserslautern

Boris Rozovsky, Ford Foundation Professor of Applied

Mathematics, Brown University

Olivier Scaillet, Professor of Finance and Statistics,

Swiss Finance Institute

George Tauchen, William Henry Glasson Professor of

Economics and Finance, Duke University

graduate students

Long-term visitors:

Heather Battey, University of Cambridge

Kai Du, School of Mathematical Sciences, Fudan University

Andrea Karlová, Stochastic Informations, Institute of

Information Theory and Automation (UTIA)

Henning Marxen, Department of Mathematics, Technische

Universität Kaiserslautern

Jian Su, University of Illinois, Chicago

Jin Zhang, University of Illinois, Chicago

Short-term visitors:

Yunjiang Jiang, Department of Mathematics, Stanford University

Phillip Monin, The University of Texas at Austin

practitioners

Long-term visitors:

Tim Hoggard, visiting research fellow

Sushant Vale, Tata Consulting Services

v i S i T o R S

24

The Institute has hosted a number of visitors over the past year – many come to give

seminars while others come to work on collaborative projects with OMI members.

C“I had heard about the Institute and how well it manages to

Cintegrate the strengths of the University in Cmathematics, statistics, economics and finance,

but the reality is even better. The physical setup Iis superb; the facilities and amenities world-class. I had a good chance to participate in a series of

presentations by Ph.D candidates over in the maths Cdepartments, and a chance to discuss research issues

Cwith several faculty and visitors at OMI.”

CIoannis Karatzas

25

VISITORS

“OMI is a very good place to do research. There is a lot going on: many visitors, many seminars and plenty of

opportunities to meet people. That is what makes OMI interesting – the presence of “Practitioners” and/or “quants” enables

discussions on problems that are not always treated in academic surroundings.

Having so many visitors and university people around ensures there is always

an audience for good discussion.”

Fred Delbaen

“I was very impressed with the level of scholarship and the

research at OMI. The researchers and faculty were eager to exchange ideas and discuss

their work. It was intellectually rewarding to visit the Institute.”

Marco Avellaneda

“I immensely enjoyed my visit to OMI. I was able to meet and interact

with several researchers, either directly in my field or in allied fields. The interdisciplinary character of OMI was most rewarding. I found

the seminars by external visitors extremely interesting and helpful. For instance I learned from one speaker, the details of the “Billion Prices” project at MIT, and I was honoured to

join the dinner group for that speaker. The hospitality was warm and thoughtful.

I would truly enjoy visiting again.”

George Tauchen

“My talk at OMI was very well attended by

mathematicians of various ages. I got a very positive reaction

and enjoyed some interesting discussions on several

mathematical questions of joint interest.”

Etienne Pardoux

“My visit was intellectually stimulating and delightful, with a lunch, seminar, and meetings

with the faculty. I was impressed by the range of issues which the faculty was working on, and the

excellent research facilities and environment at OMI.”

Suresh Sundaresan

C“I had heard about the Institute and how well it manages to

Cintegrate the strengths of the University in Cmathematics, statistics, economics and finance,

but the reality is even better. The physical setup Iis superb; the facilities and amenities world-class. I had a good chance to participate in a series of

presentations by Ph.D candidates over in the maths Cdepartments, and a chance to discuss research issues

Cwith several faculty and visitors at OMI.”

CIoannis Karatzas

sam cohenCohen, S.N., Ji, S. and Peng, S. 2011. Sublinear Expectations and Martingales in Discrete Time.

Cohen, S.N. 2011. Representing Filtration Consistent Nonlinear Expectations as G-Expectations in General Probability Spaces.

Cohen, S.N., Elliott, R.J. and Siu, T.K. 2011. Backward Stochastic Difference Equations for Dynamic Convex Risk Measures on a Binomial Tree.

martin gouldGould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. 2011. Limit Order Books.

Gould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. 2011. Statistical Properties of Forging Exchange Limit Order Books.

georg gottlobGottlob, G. 2011. On Minimal Constraint Networks.

Benedikt, M., Gottlob, G. and Senellart, P. 2011. Determining Relevance of Accesses at Runtime (Extended Version).

jan hendrik witte Witte, J.H. and Reisinger, C. 2010. On the Penalisation Error for American Options in a Jump Model.

Witte, J.H. and Reisinger, C. 2010. On the Use of Policy Iteration as an Easy Way of Pricing American Options.

Witte, J.H. and Reisinger, C. 2010. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance.

ni haoHao, N. and Lyons, T. 2011. Expected Signature of Brownian Motion up to the First Exit Time of the Domain.

vicky henderson Henderson, V., Sun, J. and Whalley, E. 2011. Portfolios of American Options under General Preferences: Results and Counterexamples.

Henderson, V. and Hobson, D. 2009. Risk Aversion, Indivisible Timing Options and Gambling.

Henderson, V. 2009. Partial Liquidation and the Disposition Effect.

tim jenkinsonJenkinson, T.J. and Stucke, R. 2011. Who Benefits from the Leverage in LBOs?

Jenkinson, T.J., Axelson, U., Stromberg, P. and Weisbach, M. 2010. Borrow Low, Buy High? The Determinants of Leverage and Pricing in Buyouts.

dmitry kramkovKramkov, D. and Predoiu, S. 2011. Integral Representation of Martingles and Endrogenous Completeness of Financial Models.

Kramkov, D and Bank, P. 2011. A Model for a Large Investor Trading at Market Indifference Prices.

anthony lee Lee, A., Caron, F. Doucet, A. and Holmes, C. 2011. A Hierarchical Bayesian Framework for Constructing Sparsity-Inducing Priors.

Lee, A., May, B.C., Korda, N. and Leslie, D. N. 2011. Optimistic Bayesian Sampling in Contextual-Bandit Problems.

Lee, A., Caron, F., Doucet, A. and Holmes, C. 2011. Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors.

anthony ledfordLedford, A. and Ramos, A. 2011. Markov Modelling of the Within Time Series Dependence. Presented at Environmental Risk and Extreme Events, Ascona.

colin mayerMayer, C. 2011. Mobile Banking and Financial Inclusion: The Regulatory Lessons.

Mayer, C. 2011. Savings as Forward Payments: Innovations on Mobile Money Platforms.

Mayer, C. 2010. Regulatory Sanctions and Reputational Damage in Financial Markets.

thomas noeNoe, T., Banerjee, S. and Bhattacharyya, S. 2011. Pumping up the SEO: The Rewards of Uninformed Speculation.

han ozsoylevOzsoylev, H., Walden, J., Yavuz, D. and Bildik, R. 2011. Investor Networks in the Stock Market. Mimeo.

wei panPan, W. Application of Cubature Method to TARN Option Pricing. Pricing Digital Option Using Cubature Method.

w o R k i n G PA P E R S

26

tarun ramadoraiRamadorai, T. and Patton, A. 2011. On the High-Frequency Dynamics of Hedge Fund Risk Exposures. Internet Appendix.

Ramadorai, T. 2010. On the Dynamics of Hedge Fund Risk Exposures.

Ramadorai, T. and Streatfield, M. 2011. Money for nothing? Understanding Variation in Reported Hedge Fund Fees.

Ramadorai, T., Jotikasthira, P. and Lundblad, C. 2011. Asset Fire Sales and Purchases and the International Transmission of Financial Shocks.

Ramadorai, T., Watugala, S. and Albuquerque, R. 2011. Trade Credit and International Return Comovement.

Ramadorai, T., Acharya, V. and Lochstoer, L. 2010. Limits to Arbitrage and Hedging: Evidence from Commodity Markets.

christoph reisinger Reisinger, C. and Giles, M. 2011. Stochastic Finite Differences and Multilevel Monte Carlo for a Class of SPDEs in Finance.

Reisinger, C. and Bujok, K. 2011. Valuation of Basket Credit Derivatives in Structural Jump-Diffusion Models.

Reisinger, C. and Gupta, A. 2011. Robust Calibration of Financial Models Using Bayesian Estimators.

daniel schwarzSchwarz, D. and Howison, S. 2011. Structural Modelling of Carbon Emission Markets.

Schwarz, D. and Howison, S. 2011. Asymptotic Analysis of Pricing Models for Carbon Emission Markets.

Schwarz, D., Carmona, R. and Coulon, M. 2011. Structural Modelling of Clean Spread Options and the Valuation of Power Plants.

lukas szpruchSzpruch, L. and Giles, M. 2011. A Note on Milstein Fundamental Theorem for Non-linear SDEs.

Szpruch, L. and Giles, M. 2011. Efficient Multilevel Monte Carlo Simulations of Non-Linear Financial SDEs without a Need of Simulation of Levy Areas.

Szpruch, L. and Mao, X. 2011. Strong Convergence and Stability of Numerical Methods for Non-Linear Stochastic Differential Equations under Monotone Condition.

Szpruch, L. and Mao, X. 2011. Strong Convergence Rates for Backward Euler-Maruyama Method for Dissipative-type Stochastic Differential Equations with Super-Linear Diffusion Coefficients.

ruediger stuckeStucke, R., Harris, B. and Jenkinson, T. 2010. A White Paper on Private Equity Research and Data.

Stucke, R. 2010. Does Private Equity Underperform or Outperform Public Equity?

Stucke, R. and Higson, C. 2010. The Private Equity Performance Puzzle.

suresh sundaresanSundaresan, S. and Wang, Z. 2011. On the Design of Contingent Capital with Market Trigger.

sumudu watugalaWatugala, S. W., Albuquerque, R. and Ramadorai, T. 2011. Trade Credit and International Return Comovement.

weijun xuXu, W. And Jiang, Y. 2010. On Number of Turns in Reduced Random Lattice Paths.

yuan xiaXia, Y. and Giles, M. 2010. Multilevel Path Simulation for Jump-Diffusion SDEs.

thaleia zariphopoulouZariphopoulou, T., Musiela, M. and Sokolova, E. 2010. Indifference Valuation under Forward Valuation Criteria: The Case Study of the Binomial Model.

Zariphopoulou, T., Leung, T. and Sircar, R. 2011. Forward Indifference Valuation of American Options, submitted for publication.

Zariphopoulou, T. and Nadtochiy, S. 2011. A Class of Homothetic Forward Investment Process with Non-Zero Volatility, submitted for publication.

Zariphopoulou, T. and Kallblad, S. 2011. On the Forward and Backward Portfolio Problem in Log-Normal Markets.

Zariphopoulou, T., Kallblad, S. and Malamud, S. 2011. Qualitative Properties of Optimal Portfolios in Log-Normal Markets.

Zariphopoulou, T. and Kallblad, S. 2011. Forward Optimal Portfolios.

lan zhangZhang, L., Li, Y., Mykland, P., Renault, E. and Zheng, X. 2011. Realized Volatility when Sampling Times can be Endogenous. In revision for Econometric Theory.

Zhang, L. 2011. What You Don’t Know Cannot Hurt You: On the Detection of Small Jumps.

Zhang, L. and Mykland, P. 2011. Between Data Cleaning and Inference: Pre-Averaging and Other Robust Estimators of the Efficient Price.

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PAPERS

sylvestre burgos

Burgos, S. Expected. 2010. Computing Greeks Using Multilevel Path Simulations, Monte Carlo and Quasi-Monte Carlo Methods, Springer Verlag, to appear.

andrea calì

Cali, A. and Pieris, A. 2011. On Equality-Generating Dependencies in Ontology Querying (extended abstract), Proc. of SEBD.

Cali, A., Gottlob, G. and Pieris, A. 2011. New Expressive Languages for Ontological Query Answering, Proc. of AAAI, to appear.

Cali, A., Gottlob, G. and Pieris, A. 2011. Querying Conceptual Schemata with Expressive Equality Constraints, Proc. of ER, to appear.

Cali, A., Gottlob, G. and Pieris, A. 2011. An Ontological Query Answering under Expressive Entity-Relationship Schemata, Information Systems Journal, to appear.

Cali, A., Gottlob, G., Kifer, M., Lukasiewic, T. and Pieris, A. 2010. Ontological Reasoning with F-Logic Lite and its Extentions. Proc. of AAAI.

Cali, A., Gottlob, G. and Pieris, A. 2010. Query Rewriting Under Non-Guarded Rules, Proc. of AMW.

Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering Under Expressive Entity-Relationship Schemata. Proc. of ER,. 347-361.

Cali, A., Gottlob, G., Pieris, A., Marnette, B. and Lukasiewicz, T. 2010. Datalog±: A Family of Logical Knowledge Representation and Query Languages for New Applications. Proc. of LICS,. 228-242.

Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering under Non-Guarded Rules in Datalog±:. Proc. Of RR,. 1-17.

Cali, A., Gottlob, G. and Pieris, A. 2010. Advanced Processing for Ontological Queries. PVLDB 3 (1),. 554-565.

thomas cass

Cass, T., Litterer, C. and Lyons, T. Rough Paths on Manifolds. (New Trends in Stochastic Analysis and Related) Topics, Worlds Scientific Press, to appear.

Cass, T. 2009. Smooth Densities for Stochastic Differential Equations with Jumps. Stochastic Process. Appl, no.5, 1416-1435.

sam cohen

Cohen, S.N. and Elliott, R.J. Existence, Uniqueness and Comparisons for BSDEs in General Spaces, in Annals of Probability, to appear.

Cohen, S.N. and Elliott, R.J. Backward Stochastic Difference Equations and Nearly-Time-Consistent Nonlinear Expectations, SIAM Journal of Control and Optimization, 49, 125-139.

Cohen, S.N., Elliott, R.J. and Pearce, C.E.M. A General Comparison Theorem for Backward Stochastic Differential Equations, Advances in Applied Probability, 42(3), 878-898.

thomas flury

Flury, T. 2010. Econometrics of Dynamic Non-Linear Models in Macroeconomics and Finance; DPhil thesis, University of Oxford.

mike giles

Giles, M., Klingbeil, G. and Erban, R. 2011. Fat vs. Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactionism, Transactions on Parallel and Distributed Systems, to appear.

Giles, M., Klingbail, G. and Erban, R. 2011. Parallel Stochastic Simulation for the Systems Biology Toolbox 2 for MAT- LAB, Bioinformatics, to appear (subject to minor revisions).

Giles, M. 2011. Approximating the Erfinv Function, GPU Compute Gems, volume 2, Morgan Kaufmann, to appear.

Giles, M., Bradley, T., Du Toit, J., Tong, R. and Woodhams, P. 2011. Parallelisation Techniques for Random Number, GPU Computing Gems, 1, Morgan Kaufmann, to appear.

georg gottlob

Gottlob, G., Sellers, A. J., Furche, T., Grasso, G. and Schallhart, C. 2011.Taking the OXPath Down the Deep Web. EDBT, 542-545.

Gottlob, G., Aschinger, M., Drescher, C., Jeavons, P. and Thorstensen, E. 2011. Structural Decomposition Methods and What They are Good For. STACS, 12-28.

Gottlob, G., Sellers, A. J., Furche, T., Grasso, G. and Schallhart, C. 2011. OXPath: Little Language, Little Memory, Great Value. WWW (Companion Volume), 261-264.

Gottlob, G., Pichler, R. and Savenkov, V. 2011. Normalization and Optimization of Schema Mappings. VLDB J, 20(2), 277-302.

Gottlob, G., Cali, A., Kifer, M., Lukasiewic, T. and Pieris, A. 2010. Ontological Reasoning with F-Logic Lite and its Extentions. Proc. Of AAAI.

Gottlob, G., Cali, A. and Pieris, A. 2010. Query Rewriting under Non-Guarded Rules. Proc. of AMW.

Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering under Expressive Entity-Relationship Schemata. Proc. of ER, 347-361.

Gottlob, G., Cali, A., Pieris, A., Marnette, B. and Lukasiewicz, T. 2010. Datalog±: A Family of Logical Knowledge Representation and Query Languages for New Applications. Proc. Of LICS, 228-242.

Gottlob, G., Cali, A. and Pieris, A. 2010. Query Answering under Non-Guarded Rules in Datalog±:. Proc. of RR, 1-17.

Gottlob, G., Cali, A. and Pieris, A. 2010 Advanced Processing for Ontological Queries. PVLDB 3 (1), 554-565.

ben hambly

Hambly, B.M., Bush, N., Haworth, H., Jin, L. and Reisinger, C. Stochastic Evolution Equations in Portfolio Credit Modelling, SIAM J. Fin. Math, to appear.

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Hambly, B.M., Biggins, J.D., and Jones, O.D. 2011. Multifractal Spectra for Random Self-Similar Measures via Branching Processes, Adv. Appl. Prob, 43, 1-39.

Hambly, B.M. 2011. Asymptotics for Functions Associated with Heat Flow on the Sierpinski Carpet, Canadian J. Math, 63, 153-180.

Hambly, B.M. and Croydon, D.A. 2010. Spectral Asymptotics for Stable Trees, Elec. J. Probab, 15, 1772-1801.

vicky henderson

Henderson, V. and Hobson, D. 2011. Optimal Liquidation of Derivative Portfolios, Mathematical Finance, to appear.

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

jan hendrik witte

Witte, J.H. and Reisinger, C. 2011. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance, SIAM Journal of Numerical Analysis, 49(1), 213—231.

chris holmes

Holmes, C. ,Yau, C., Papaspiliopoulos, O. and Roberts, G. 2011. Bayesian Non-Parametric Hidden Markov Models with Applications in Genomics, J Royal Stat Soc, Series B 73 (Part 1), 33-57.

Holmes, C., Hjort, N., Muller, P. and Walker, S. 2010. Bayesian Nonparametrics. Cambridge University Press.

tim jenkinson

Jenkinson, T.J., Abrahamson, M., and Jones, H. 2011. Why don’t U.S. Issuers Demand European Fees for IPOs?, Journal of Finance, to appear.

Jenkinson, T.J. and Sousa, M. 2011. Why SPAC Investors should listen to the Market?, Journal of Applied Finance, to appear.

hanqing jin

Jin, H., Dai, M. and Liu, H. 2011. Illiquidity, Position Limits, and Optimal Investment for Mutual Funds, Journal of Economic Theory, to appear.

Jin, H. and Zhou, X. 2011. Greed, Leverage, and Potential Losses: A Prospect Theory Perspective, Mathematical Finance, to appear.

Jin, H., Zhang, S., Hanqing, J., Zhang, S. and Yu Zou, X. 2011. Behavioural Portfolio Selection with Bounded Loss, Acta Mathematica Sinica.

Jin, H., Dai, M., Zhong, Y. and Yu Zhou, X. 2010. Buy Low and Sell High. Contemporary Quantitative Finance, 317-334.

ada lau

Lau, A. and McSharry, P. 2010. Approaches for Multi-Step Density Forecasts with Application to Aggregated Wind Power, Annals of Applied Statistics, 4, (3), 1311–1341.

Lau, A., Baaquie, B. E., Cao, Y. and Tang, P. 2011. Path Integral for Equities: Dynamic Correlation and Empirical Analysis, Physica A, to appear.

anthony ledford

Ledford, A. and Ramos, A. 2011. An Alternative Point Process Framework for Modelling Multivariate Extreme Values, Communications in Statistics - Theory and Methods, 40, (12), 2205 – 2224.

anthony lee

Lee, A. 2010. Comment on Particle Markov Chain Monte Carlo Methods. J. Royal Statistical Soc. B.

Lee, A. 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation with Advanced Monte Carlo Methods. JCGS.

gechuan liang

Liang, G., Lyons, T. and Qian, Z. Backward Stochastic Dynamics on a Filtered Probability Space, Annals of Probability, to appear.

Liang, G. and Jiang, L. A Modified Structural Model for Credit Risk, IMA Journal of Management Mathematics, to appear.

jeremy large

Large, J. 2011. Estimating Quadratic Variation when Quoted Prices Change by a Constant Increment, Journal of Econometrics, 160, 2-11.

terry lyons

Lyons, T., Cass, T. and Litterer, C. 2011 Integrability Estimates for Gaussian Rough Differential Equations, 1-23, arXiv: 1104.1813

Lyons, T., Cass, T. and Litterer, C. 2011. Rough Paths on Manifolds, New Trends in Stochastic Analysis and Related Topics, A volume in Honour of Prof K.D. Elworthy, arXiv: 1102.0998v1

Lyons, T. and Hao, N. 2011. Expected signature of two dimensional Brownian Motion up to the first exit time of the domain. Pgs. 1-21 arXiv: 1101.5902

Lyons, T., Liang, G. and Qian, Z. 2010. A Functional Approach to FBSDEs and Its Application in Optimal Portfolios, arXiv: 1011.4499

Lyons, T. and Litterer, C. 2010. High order recombination and an application to cubature on Wiener space, arXiv: 1008.4942

michael monoyios

Monoyios, M. and Ng, A. 2011. Optimal Exercise of an Executive Stock Option by an Insider, International Journal of Theoretical and Applied Finance, 1483-106.

Monoyios, M., Ng, A. and Danilova, A. 2010. Optimal Investment with Inside Information and Parameter Uncertainty, Mathematics and Financial Economics, 3, 13-38.

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RESEARCH

Monoyios, M. 2010. Utility-Based Valuation and Hedging of Basis Risk with Partial Information, Applied Mathematical Finance, 17, 519-551.

josé martinez

Martinez, J. and Sandleris, G. 2011. Is it Punishment? Sovereign Defaults and the Declines in Trade, Accepted, Journal of International Money and Finance, to appear.

Martinez, J. 2010. Information Misweighting and the Cross Section of Stock Recommendations, Journal of Financial Markets, to appear.

per mykland

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

Mykland, P.A., Ait-Sahalia, Y., and Zhang, L. 2011. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise, Journal of Econometrics, 160, 160-165.

Mykland, P.A., Zhang, L., and Aït-Sahalia, Y. 2011. Edgeworth Expansions for Realized Volatility and Related Estimators, Journal of Econometrics, 160, 190-203.

Mykland, P.A., and Zhang, L. 2011. The Double Gaussian Approximation for High Frequency Data, Scandinavian Journal of Statistics, to appear.

thomas noe

Noe, T. 2010. Where Did all the Dollars Go? The Effect of Cash Flows on Capital and Asset Structure, Forthcoming in the Journal of Financial and Quantitative Analysis, to appear.

Noe, T. 2009. Tunnel-Proofing the Executive Suite: Transparency, Temptation, and the Design of Executive Compensation, Review of Financial Studies, 22, 4849-4880 (lead article).

Noe, T. 2009. Stock Market Liquidity and Firm Performance: Wall Street Rule or Wall Street Rules? (with Fang, V. and Tice, S.), Journal of Financial Economics, 94, 150-169.

diaa noureldin

Noureldin, D., Shephard, N. and Sheppard, K. 2011. Multivariate High-Frequency-Based Volatility (HEAVY) Models. Journal of Applied Econometrics, to appear.

han ozsoylev

Ozsoylev, H. and Werner, J., 2011. Liquidity and Asset Prices in Rational Expectations Equilibrium with Ambiguous Information, Economic Theory, to appear.

cavit pakel

Pakel, C., Shephard N. and Sheppard K., 2011. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models, Statistica Sinica, 21, 307-329.

andrew patton

Patton, A. and Timmermann, A. Predictability of Output Growth and Inzation: A Multi-Horizon Survey Approach, Journal of Business and Economic Statistics, to appear.

Patton, A. 2011. Data-Based Ranking of Realised Volatility Estimators, Journal of Econometrics, 161(2), 284-303.

Patton, A. 2011. Volatility Forecast Comparison using Imperfect Volatility Proxies, Journal of Econometrics, 160(1), 246-256.

Patton, A. and Timmerman, A. 2010. Why do Forecasters Disagree? Lessons from the Term Structure of Cross-Sectional Dispersion, Journal of Monetary Economics, 57(7), 803-820.

Patton, A. and Timmerman, A. 2010. Monotonicity in Asset Returns: New Tests with Applications to the Term Structure, the CAPM and Portfolios Sorts, Journal of Financial Economics, 98(3), 605-625.

stephen roberts

Roberts, S., Yoon, J.W., Dyson, M. and Gan, J. 2011. Bayesian Inference for an Adaptive Ordered Probit Model: an Application to Brain Computer Interfacing, Neural Network, to appear.

Roberts, S., Karastergiou, A., Johnston, S., Lee, H., Weltevrede, P. and Kramer, M. 2011. A Transient Component in the Pulse Profile of PSR J0738-4042, Monthly Notices of the Royal Astronomical Society, to appear.

Roberts, S. and Ebden, M. 2011. Graph Marginalization for Rapid Assignment in Widearea Surveillance, Adhoc Networks Journal 9(2), 180-8, to appear.

Roberts, S., Psorakis, I. and Ebden, M. 2011. Overlapping Community Detection using Bayesian Non-Negative Matrix Factorization, Physical Review E, in press.

Roberts, S., Fox, C. 2011. A Tutorial on Variational Bayesian Inference. Artificial Intelligence Review, Spinger, in press.

Roberts, S. Yoon, J.W. Dyson, M. and Gan, J. 2011. Bayesian Inference for an Adaptive Ordered Probit model: an Application to Brain Computer Interfacing, Neural Networks, in press.

Roberts, S., Karastergiou, A., Johnston, S., Lee, H., Weltevrede, P. and Kramer, M. 2011. A Transient Component in the Pulse Profile of PSR J0738-4042, Monthly Notices of the Royal Astronomical Society, B0736-40.

Roberts, S. and Ebden, M. 2011. Graph Marginalization for Rapid Assignment in Wide-Area Surveillance, Adhoc Networks Journal 9(2), 180-8.

Roberts, S., Reece, S., Nicholson D. and Lloyd, C. 2011. Determining Intent using Hard/Soft Data and Gaussian Process Classifiers, Proceedings of Fusion.

Pickup, L., Capel, D., Roberts, S. and Zisserman, A. 2010. Multiframe Super-Resolution from a Bayesian Perspective, In Super-Resolution Imaging, Chapter 9, CRC Press, 247-284.

Roberts, S. and Reece, S. 2010. The Near Constant Acceleration Gaussian Process Kernel for Tracking, IEEE Signal Processing Letters, 17(8), 707-710.

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Roberts, S., Ebden, M. and Stranjak, A. 2010. Visualizing Uncertainty in Reliability Functions with Application to Aero Engine Overhaul, Journal of the Royal Statistical Society C. Volume 59, part 1, 163-173.

Roberts, S. Garnett, R. Osborne, M. A. Reece, S. and Rogers, A. 2010. Sequential Bayesian Prediction in the Presence of Changepoints and Faults, The Computer Journal, 53(9), 1430-1446.

Roberts, S. and Yoon, J. W. 2010. Robust Measurement Validation in Target Tracking using Geometric Structure, IEEE Signal Processing Letters, 17(5), 493-496.

Roberts, S. and Lee, S. M. 2010. Sequential Dynamic Classification using Latent Variable Models, The Computer Journal, 53, 1415-1429.

Roberts, S., Lowne, D. and Garnett, R. 2010. Sequential Non-Stationary Dynamic Classification with Sparse Feedback, Pattern Recognition, 43, (3)0, March 2010, 897-905.

Psorakis, I., Roberts, S. and Sheldon, B. 2010. Soft Partitioning in Networks via Bayesian Non-Negative Matrix Factorization, Proceedings of NIPS 2010 workshop on community detection.

Reece,S., Mann,R., Rezek. I. and Roberts, S. 2010. Gaussian Process Segmentation of Co-Moving Animals, 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering , Chamonix, France.

Kaufman, M. and Roberts, S. 2010. Coordination vs. Information in Multi-Agent Decision Processes, Proceedings of AAMAS 2010.

McInerney, R., Roberts, S. and Rezek, I. 2010. Sequential Bayesian Decision Making for Multi-Armed Bandit, Proceedings of AAMAS 2010.

Roberts, S. and Reece, S. 2010. An Introduction to Gaussian Processes for the Kalman Filter Expert, Proceedings of Fusion 2010.

Roberts, S., Garnett, R. and Osborne, M. A. 2010. Bayesian Optimization for Sensor Set Selection, Proceedings of IPSN 2010, Stockholm.

Roberts, S. and Ebden, M. 2010. Graph Marginalization for Rapid Assignment in Wide-Area Surveillance. International Conference on Ad Hoc Networks, Niagara Falls, Canada, LNICST (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 28, 691-703.

christoph reisinger

Reisinger, C., Bush, N. Hambly, B.M. Haworth, H. and Jin, L. 2011. Stochastic Evolution Equations in Portfolio Credit Modelling, SIAM Journal on Financial Mathematics, to appear.

Reisinger, C. and Witte, J.H. 2011. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance, SIAM Journal of Numerical Analysis, 49(1), 213-231.

tarun ramadorai

Ramadorai, T. 2011. Capacity Constraints, Investor Information, and Hedge Fund Returns, Journal of Financial Economics, to appear. Previously entitled “Investor Interest and Hedge Fund Returns.”

Ramadorai, T. 2010. The Secondary Market for Hedge Funds and the Closed Hedge Fund Premium, Journal of Finance, to appear. Internet Appendix.

neil shephard

Shephard, N., Noureldin, D. and Sheppard, K. 2011. Multivariate High-Frequency-Based Volatility (HEAVY) Models, Journal of Applied Econometrics, to appear.

Shephard, N. and Flury, T. 2011.Bayesian Inference Based only on a Simulated Likelihood, Econometric Theory, to appear.

Shephard, N., Barndorff-Nielsen, O. E., Lunde, A. and Hansen, P.R. 2011. Subsampling Realised Kernels, Journal of Econometrics, 160, 204-219.

Shephard, N. Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading, Journal of Econometrics, to appear.

Shephard, N. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models, forthcoming Statistica Sinica.

Shephard, N. Bayesian Inference Based only on Simulated Likelihood: Particle Filter.

Shephard, N. 2010. Deferred Fees for Universities, Economic Affairs, 30, (2), 40-44.

Shephard, N. and Barndorff-Nielsen, O.E. 2010. Volatility, in Encyclopedia of Quantitative Finance, edited by Rama Cont, John Wiley and Sons Ltd, Chichester, UK, 1898-1901.

Shephard, N., Barndorff-Nielsen, O. E. and Kinnebrouck, S. 2010. Measuring Downside Risk: Realised Semivariance, in Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle, edited by T. Bollerslev, J. Russell and M. Watson (eds), Oxford University Press, 117-136.

Shephard, N. 2010. Realising the Future: Forecasting with High Frequency Based Volatility (HEAVY) Models, Journal of Applied Econometrics, 25, 197-231.

torsten schöneborn

Schöneborn, T. and Martin, R. 2011. Mean Reversion Pays, but Costs. RISK, 96-101.

suresh sundaresan

Sundaresan, S., Tonetti, C., Bartolini, L. and Hilton, S. 2011. Collateral Values by Asset Class: Evidence from Primary Securities Dealers, Financ. Stud, (2011) 24(1), 248-278.

Sundaresan, S., Asvanunt, A. and Broadie, M. 2011 Managing Corporate Liquidity: Strategies and Pricing Implications, International Journal of Theoretical and Applied Finance, 14, (3), 369-406.

nithum thain

Thain, N., Mirrokni, V., and Vetta A., 2011. On the Implications of Lookahead Search in Game Playing.

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zhongmin qian

Qian, Z. and Tudor, J. 2011. Differential Structure and Flow Equations on Rough Path Space, arXiv:1102.561, to appear.

Qian, Z. Tudor, J. and Cass, T. 2011. Non-Linear Evolution Equations Driven by Rough Paths, arXiv:0911.0281, to appear.

Qian, Z. and Ying, J. 2011. Martingale Representations for Diffusion Processes and Backward Stochastic Differential Equations, arXiv:0910.4911, to appear in Sem de Probab.

Qian, Z., Zheng, W. and Duan, X.L. 2011. On Local Linear Approximations to Diffusion Processes, to appear in International Journal of Mathematics and Mathematical Sciences.

thaleia zariphopoulou

Zariphopoulou, T. and Sircar, R. 2010.Utility Valuation of Credit Derivatives and Applications to CDOs, Quantitative Finance, 10 195-208.

Zariphopoulou, T., Sokolova, K. and Musiela, M. 2010. Indifference Valuation in Incomplete Binomial Models, Mathematics in Action, 3(2), 1-36.

Zariphopoulou, T. and Musiela, M. 2010. Portfolio Choice Under Space-Time Monotone Performance Criteria, SIAM Journal on Financial Mathematics,1, 326-365.

Zariphopoulou, Z. and Zitkovic, G. 2010. Maturity-Independent Risk Measures, SIAM Journal on Financial Mathematics,1, 266-288.

Zariphopoulou, T. and Musiela, M. 2010. Stochastic Partial Differential Equations and Portfolio Choice, Contemporary Quantitative Finance, Springer-Verlag, 195-215.

Zariphopoulou, T. and Musiela, M. 2010. Initial Investment Choice and Optimal Future Allocations Under Time-Monotone Performance Criteria, International Journal of Theoretical and Applied Finance, 14(1), 61-81.

lan zhang

Zhang, L. 2011. Estimating Covariation: Epps Effect, Microstructure Noise, Journal of Econometrics, 160, 33-47.

Zhang, L., Mykland, P.A., and Ait-Sahalia, Y. 2011. Edgeworth Expansions for Realized Volatility and Related Estimators, Journal of Econometrics, 160, 190-203.

Zhang, L. Mykland, P.A. and Ait-Sahalia, Y. 2011. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise, Journal of Econometrics, 160, 160-175.

Zhang, L., Kang, Z.X. and Chen, R. 2010. Forecasting Return Volatility in the Presence of Microstructure Noise, Statistics and its Interface, 3 (2), 145-158.

Zhang, L. Implied and Realized Volatility: Empirical Model Selection. 2010. Annals of Finance, to appear.

Zhang, L., and Chen, R. Kang, Z.X. 2010. Forecasting Return Volatility in the Presence of Microstructure Noise, Statistics and Its Interface. 3 (2), 145-158.

Zhang, L. and Mykland, P.A. 2010. The Econometrics of High Frequency Data. Statistical Methods for Stochastic Differential Equations, to appear.

yifei zhong

Dai, M. and Zhong, Y. 2010. Penalty Methods for Continuous-Time Portfolio Selection with Proportional Transaction Costs, Journal of Computational Finance, 13(3), 1-31.

Dai, M. and Zhong, Y. 2010. Optimal Stock Selling/Buying Strategy with Reference to the Ultimate Average, Mathematical Finance, to appear.

Dai, M, Jin, H., Zhong, Y. and Zhou, X. 2010. Buy Low and Sell High, Contemporary Quantitative Finance: Essays in Honour of Eckhard Platen, Springer, 317-334.

Dai, M, Zhong, Y. and Kwok, Y.K. 2011. Optimal Arbitrage Strategies on Stock Index Futures under Position Limits. Journal of Futures Markets, 31, 394-406.

Bian, B., Dai, M., Jiang, L., Zhang, J. and Zhong, Y. 2011. Optimal Decision for Selling an Illiquid Stock, Journal of Optimization Theory and Application, to appear.

xunyu zhou

Zhou, X. and Jin, H. 2011. Greed, Leverage, and Potential Losses: A Prospect Theory Perspective, to appear in Mathematical Finance.

Zhou, X., Meyer-Brandis, T. and Øksendal, B. 2011. A Mean-Field Stochastic Maximum Principle via Malliavin Calculus, Stochastics, (A Special Issue for Mark Davis’ Festschrift), to appear.

Zhou, X. and He, X. 2011. Portfolio Choice Under Cumulative Prospect Theory: An Analytical Treatment, Management Science, 57, 315-331.

Zhou, X. and He, X. 2011. Portfolio Choice via Quantiles, Mathematical Finance, 21 (2011), 203-231.

Zhou, X. Jin, H. and Zhang, S. 2011. Behavioral Portfolio Selection with Loss Control, Acta Mathematica Sinica, 27, 255-274. (A Special Issue Dedicated to Loo-Keng Hua on his 100th Birthday).

Zhou, X. and Chiu, C. 2011. The Premium of Dynamic Trading, Quantitative Finance, 11, 115-123.

Zhou, X. 2010. Mathematicalising Behavioural Finance, Proceedings of the International Congress of Mathematicians, Hyderabad, India.

Zhou, X., Dai, M. , Jin, H. and Zhong, Y. 2010. Buy Low and Sell High, Contemporary Quantitative Finance, Edited by Carl Chiarella and Alexander Novikov, Springer, 317-334. (Essays in Honour of Eckhard Platen).

Zhou, X. and Ji, S. 2010. A Generalized Neyman-Pearson Lemma for G-Probabilities. Probability Theory and Related Fields, 148, 645-669.

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In October 2010, the company acquired GLG Partners to create

a broader mix of fund strategies and products for investors. The

combined firm now has expertise in a wide range of investment

styles including managed futures, equity, credit, emerging

markets, global macro and multi-manager – all of which share a

relentless focus on investment performance.

Since the acquisition, new offerings combining the strengths of

the two businesses have been developed. In February 2011, the

company announced the launch of Man IP 220 GLG, a structured

product which offers investors access to a combination of Man’s

flagship managed futures manager, AHL, and a broad range of

GLG’s discretionary strategies for the first time.

Then in June, Man launched the Man GLG Multi-Strategy fund

– a unique combined fund which offers investors access to a

range of Man and GLG funds which comply with European

UCITS regulations. This latest fund launch was a particular

success attracting 100 million in commitments.

Japan fund launch

Perhaps the most significant fund launch in the last year,

however, was the successful launch of an AHL open-ended fund

in Japan called Nomura Global Trend, which raised an initial

US$1.5 billion. The fund began trading at the end of April 2011.

Alongside this growth in new funds and products, Man has

also been investing in its people, technology and operations

underpinning trading. In May, Man’s trend following CTA

business, AHL, became the first CTA to create a standalone

trading team in Hong Kong. AHL staff in Oxford can see this

team on large LCD screens via a video link, and the Hong Kong

trading team have a similar view of the Oxford team, binding

together research and trading teams across the globe.

New hq opens

In July, Man also unveiled new company headquarters in Swan

Lane in the City of London. The new £250m development on

the banks of the Thames houses 957 employees and includes the

latest trading and communications technology designed to enable

excellence in research, communication and trade execution.

Through this pursuit of excellence and its continued focus on

performance, Man is strongly positioned for the future, and

the commitment to the Oxford-Man Institute of Quantitative

Finance remains key.

UNIQUE

The last year has been one of transformation for man Group, a world-leading alternative investment management business, and collaborative partner for omi.