october 2011 david toback, texas a&m university research topics seminar 1 andreas gocksch bnl...

October 2011David Toback, Texas A&M University

Research Topics Seminar 1

Andreas GockschBNL Colloquium

August 2015

Life after Physics:

A look back on 20 years in Finance

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http://www-cdf.fnal.gov/

Prologue

• This talk is about a look back on a 20 year career in finance.

• In the last year though I have come come back full circle and have been working on problems in hematology/immunology.

• So there is life after life after physics!• Who knows what life after (you get the picture)

will look like!– But it all started right here at the Lab!

BNL ColloquiumAugust 2015

Andreas GockschLife after Physics 2


Prologue Continued…• I left the Lab in December of 1993 after a bit over 5 years here as

a member of the Theory Group• Many of my former colleagues are still here: Mike Creutz, Rob

Pisarski, Bill Marciano, Sally Dawson, Frank Paige, Larry Trueman, Amarjit Soni

• I have great memories of my collaborations here with: Mike Creutz, Rob Pisarski, Chris Korthals Altes, Tanmoy Bhattacharya, Sean Gavin, Masanori Okawa, Michael Ogilvie, Sinya Aoki, Rajiv Gavai, Urs Heller, Amarjit Soni, Yue Shen

• I learned a lot from them: They all in one way or another contributed to the “tool kit” that I carry around and that should later prove extremely valuable




Transition to Finance• At the end of 1993 my time at the lab was up• Very few tenure track jobs at the time and I need a plan B• At the time there was a lot of chatter (the real kind – this is

before twitter/social media) about physicists making big $s on Wall Street

• I was intrigued and ended up connecting with John Breit who had been working at Merrill Lynch for a few years

• John gave me a list of places to send my resume to and he also gave me some advice on how to prepare for interviews




Transition to FinanceWhy were the Wall Street firms looking for “quants”?• First and foremost at the time it was the explosion of the use of

financial derivatives• Need people who can understand the mathematics and write

models for pricing complex financial instruments• My only preparation before going on interviews consisted in

studying this book:




Transition to Finance continued• At the time I knew practically nothing about finance. I

distinctly remember not actually knowing what a bond was! And this despite making payments on a mortgage….

• Do you know what a bond is? This is what comes up when you google images for “bond”:




Transition to Finance continued



• Promissory note: Invest $1 now (t) and get paid back $1 at maturity (T) plus regular coupon payments

• All payments are “discounted” at the appropriate “yield”

• Price fluctuates with level of market yields





Why am I telling you this?• I ended up interviewing at 2 places: Solomon

Brothers (Tony Terrano) and Morgan Stanley• The head of fixed income research at Morgan

Stanley wanted me to pick a small project and give a seminar

• I chose the subject of yield curve fitting and proposed to use “simulated annealing”

• Find set of forward rates f which minimize the energy functional globally





• Used something I had come across in my physics past (simulated annealing) Use of “tool kit”

• Nobody had done it before• Turns out for a good reason: It does not work in

practice. Use (cubic) splines in practice – turns the problem into a linear one. Much simpler.

• Anyway, it got me the job though and I ended up at Morgan Stanley in January of 1994.






• I was hired into a group called “fixed income research”

• (Mostly) supported the interest rate derivatives trading desk

• Took a crash course in the in-house (interpreted) programing language APL (enhanced version of APL)

• Quickly realized that although I had no trouble understanding the math (“stochastic calculus”) behind the pricing of derivatives I completely lacked financial intuition

• It takes the longest to develop and some people never get it

Fixed income research at MS





• Example: Simplest “derivative” – forward contract on a stock

• Seller agrees to sell stock S today (t=0) to buyer at a fixed price K at some time in the future t=T

• What is the fair (“arbitrage free”) price K?• Math: Use “risk neutral measure” i.e.• “Numeraire” is the “bank account”• In our example • Hence • Finally• Note:






• Intuition: As the seller what should I charge?• I can borrow $S today and buy the stock• At t=T I pay back the loan – have to pay back • Hence I need to charge • This is the forward price of the stock• As the buyer I can sell (short) the stock and invest the

proceeds. At time T I buy the stock form the seller with the proceeds and “cover the short”.

• This kind of reasoning is very helpful but it won’t get you very far in figuring out the fair price of an option:






• After this quick look at no-arbitrage pricing let’s go back to the yield curve

• Spent most of my time at MS working on pricing complex derivatives by Monte Carlo within the framework of the HJM model

• Arbitrage free way of modeling the yield curve. Actually the model specifies the dynamics of the forward rates. Remember:







HJM dynamics

• Volatility here is deterministic – does not depend on rates

• In practice use discretized multi-factor log normal






HJM dynamics (“Ensemble” of 2 paths)

Today’s forward rate curve

Future forward rate curve

Example: Mortgage Pricing





• We had a 4-factor log-normal discretized HJM model . Used to price everything via Monte Carlo.

• Later moved to “square root”• Towards the end of my tenure at MS John Uglum

and I developed a 2-factor PDE version (can solve via Crank-Nicholson) of the square root version. Fast way of pricing “Bermudan Swaptions”

• Based on theoretical work by Marco Avellaneda (Courant)






• In 1998 I joined the “Cross Markets Trading Group” as a Quant/Strategist

• Like many others I wanted to trade• Goal was to use the firm’s capital to make money

trading multiple assets across multiple time zones (rates, FX, commodities). “Internal Global Macro Hedge Fund”.

• Relatively short lived (ad)venture• Russian crisis (ruble devaluation, default on domestic

debt) as a result of the still lingering (1997) Asian currency crisis

• Early 1999 got a job in risk management

Merrill Lynch 1998-2005





• All the big Wall Street firms have Risk Management departments

• Oversee Market, Credit, Operational, Liquidity risk• Report internally and to regulators• Oversee and set limits on the activities of trading

desks• Great place to see/understand the workings of

company• My first job was in “Quantitative Risk Management”

working for Bryan Lynn• Worked on the “VaR model”






• Percentile of the return (“P&L”) distribution• Probability of loss > percentile• Normal Distribution:





• Probability of loss > 99% (daily) Var is 1%. Hence expect losses greater than the Var on 2-3 days if there are 250 trading days

• Generate P&L distribution based on historical data (typically 4 years weighted towards the more recent)

• This is a huge undertaking considering the business mix, risk factors etc.

• Typical daily VaR at 95% confidence for big Wall Street Firm: $50mm

• VaR often supplemented by “stress tests” in order to get understanding of extreme tail events





• Later moved out of Quantitative Risk Management into Market Risk Management

• Reported to the head of market risk John Breit• Market risk as opposed to Credit risk which looks

at trading counterparty risk• Covered Rates, FX, Commodities and ML

treasury• Others covered equities, credit (Timo van

Ritbergen)• Robin Stuart was my FX risk manager• Also on the team: Adrian Ghinculov





• Covered the Interest Rate Derivatives trading desk• Single biggest risk taker in the firm• Had ~$20mm down days – this is one trading desk!• In those days the desk routinely made ~300mm/year• As a risk manager I still relied on my quant tool kit

but to a much lesser extent. It more or less buys you respect!

• Other skills much more important: Political, inter-personal, thick skin.

• Traders don’t exactly like to talk to risk managers!• It’s not for everyone!• Just like trading (risk taking) is not for everyone


Swiss Re 2005-2010



• In 2005 I moved to Swiss Re to become Head of Market Risk for their Capital Markets business

• Very broad business mix. Rates, Equities, Credit, Structured Products, Weather derivatives, Fund derivatives, statistical arbitrage….

• Business was basically an internal hedge fund• There was also a traditional asset management

division that invested the firms income (i.e. reinsurance fees) in equities, government bonds, mortgage bonds etc.

• After the crisis 2007/2008 I ran all of financial market risk reporting to the CRO


Swiss Re 2005-2010



• In 2009 a new CRO was brought in and I moved to the “bad bank” – working on selling off the remaining toxic assets

• The crisis years were incredibly stressful but of course also “exciting”:

• August 2007 quant crash (probably because of a large fund liquidating positions). Ordered liquidation after close to $20mm loss over a few days.

• Earnings restatement because of revaluation of “CDS on CDOs (“it’s just insurance”)


Swiss Re 2005-2010



• This structure was sold out of the “Credit Solutions” “insurance” business

• AIG did in excess of $50Bln of these structures

Insurance Seller

Client

15 bps

$ Loss in excess of 10%

$4 BlnCollection of

Subprime bonds and other reference

assets


Wrapping up



• I spent the last 4 years at CRT Capital as Chief Risk Officer

• Built out the risk function for the broker dealer• Unique challenges because relatively thinly

capitalized and highly leveraged• With $250mm of capital we supported at times

$15Bln of (mostly liquid) assets• Biggest risk was liquidity (funding) risk


Wrapping up: Now vs. then



• Solid quantitative skills are still in high demand• The physicist’s tool kit is ideal (we know computing,

programing, statistics, etc.)• Demand is not so much driven by the investment banks

anymore• There the focus is on regulatory pressures

Dodd-Frank (Volker Rule). Severe restrictions on proprietary trading

Much less demand for exotic derivatives Standard derivatives moving to central clearing

(exchanges) Fed implementation of Basel III very strict Both quality and quantity of capital that Banks need

to hold has gone up a lot


Wrapping up: Now vs. then



• A lot of the demand for quants comes from hedge funds and alternative asset managers. Right here on Long Island: Renaissance Technologies (Alan Stange)

• Algorithmic trading/statistical arbitrage Solid programing/computer science skills

• Just reading Hull is not going to get you in the door anymore• Of course it always helps to know somebody

networking• A lot of people do one year quantitative finance courses

(Columbia, NYU)• CFA, FRM certifications• There clearly is still life after Physics in Finance!• But it’s a new world out there: Consider Google, Facebook

etc.• There are incredible opportunities out there that weren’t there

in ‘94


Bibliography



General Interest:

• Edwin Lefevre – Reminiscences of a stock operator• Michael Lewis – Liars poker, The big short• Richard Bookstaber – A demon of our own design (a good

introduction to what risk management is all about)

Text Books:• John Hull – Options, Futures and other derivatives• Steven Shreve – Stochastic Calculus for Finance I and II

Research article:• Marco Avellaneda and Jeong-Hyun Lee - Statistical Arbitrage

in the U.S. Equities Market


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