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Baruch金融工程之数量金融大师Professor Jim Gatheral专访：有的放矢，瞄准华尔街
发布日期：2013-09-11 03:48 来源：北京大学国家发展研究院
Dr. Jim Gatheral于1983年取得剑桥大学理论物理博士学位，是闻名华尔街的数量金融大师，他的职业及研究经历涉及金融衍生品定价、风险控制及交易优化。在25年的职业生涯中，他曾于纽约，伦敦，东京等地从事第一线的交易工作。其中17年时间在美林银行（Bank of
America Merrill Lynch）担任董事总经理（Managing Director），负责定量分析研究。同时，从1998年至今，Jim Gatheral教授先后在纽约大学和纽约市立大学担任教职，从事金融工程领域教学工作。
Jim Gatheral教授目前的研究兴趣为：算法交易中的波动率模型和市场微结构模型。Gatheral教授同时也是世界范围内学术及业界交流会议中的常客。他的专著 The Volatility Surface: A Practitioner's Guide 是数量金融领域的经典之作。
2013年10月20日-26日，Dr. Jim Gatheral将在国家发展研究院举行金融衍生品专题讲座，与同学们一起探讨华尔街最火热的交易模型，分享最前沿的金融业界动态。为此，我们就专业知识储备、金融市场发展前景和职业导向对Dr. Jim Gatheral进行了专访。
April 11, 2013. Dr. Jim Gatheral of the Financial Engineering Master’s Program at Baruch College was named Presidential Professor at Baruch College, City University of New York.
Dr. Gatheral joined Baruch College in August 2010, after a distinguished career in finance, most recently as a Managing Director at Merrill Lynch, where he led the quantitative research group for 17 years. Before making his way to New York, Dr. Gatheral’s work spanned the trading and banking worlds, working out of London and Tokyo.
Dr. Gatheral, one of the top quants in the world and author of the best selling book “The Volatility Surface: A Practitioner’s Guide“, has strengthened the Baruch MFE Program, propelling it to be included among the top five such programs by the end of 2011. Jim’s impact on the Baruch MFE program has been significant and multi-faceted, leading directly to not only increasing the program’s prestige and standing, but also attracting even stronger students who have since won international competitions, including the Rotman International Trading Competition and the Metaquotes Automated Trading Championship.
Can you give us a recap on your education background and career?
My undergraduate degree was in Mathematics and Natural Philosophy at the University of Glasgow. My PhD was in Theoretical Physics at Cambridge University and my thesis title “Infrared Divergences in Perturbative QCD”. I joined Bank of America in London as a trainee loan officer in 1983 with the title of Assistant Cashier, receiving my first bonus of £400 in 1985. I started trading currency options at BofA in 1984 and subsequently joined Bankers Trust to trade interest rate derivatives in 1986. I ended up running currency options and then ran Capital Markets (effectively derivatives trading) in Tokyo in the early 1990s. I joined Merrill Lynch in 1993 as global head of equity derivatives trading and set up the quant team in 1996, retiring from the firm earlier this year.
Your research has recently moved into Market Microstructure. Tell us what is significant about this area that captures your interest?
What is really interesting about this area is that nothing is yet well understood, and the timing is perfect because we now have access to huge amounts of high frequency data. Obviously, with my physics background, I am particularly interested in the recent developments in econophysics. From the theoretical point of view, I think we are closer than ever to a good understanding of the process of price formation and from the practical point of view, simple physics-style models have already led to the development of more efficient algorithms that minimize transaction costs.
How do you connect Market Microstructure to Algo trading?
The way I like to define things, market microstructure is the theory underlying algorithm construction. That said, most algorithms are even now constructed without any theory – there is still a huge opportunity for quants to get involved.
What would be the area in quant finance that will experience most innovative research idea and product growth in the next few years?
If you will forgive me for talking my own book on this one, I think that the answers are market microstructure and volatility surface dynamics. More specifically, there is likely to be less emphasis on exotic derivatives in my view and more trading will take place on exchanges. This means in particular that we won’t need models to compute the prices of financial derivatives because pricing will be transparent. And if there is less inventory on dealer books, that means that options dealers will need to better understand and model the shapes and dynamics of volatility surfaces, just as option market makers attempt to do today. Up to now, models have been largely normative: We come up with some plausible process, derive some pricing formula, and fit the resulting model to market prices. In the future, models will have to have realistic dynamics, consistent with observation. Control of execution costs will also be critical and for that, a good understanding of market microstructure will be essential.
What would you advise people who want a career as a financial engineer?
My advice to such people is the same as my advice to anyone embarking on any career: Keep studying and learning, keep up with the latest research and find people to work with who will teach you something.
Where do you see a growth for people with quantitative background?
Obviously, my background is finance and I do think that there will be continued and increasing demand for people with quantitative backgrounds. I can foresee a time when it will be more or less impossible to get a trading job without a graduate qualification such as Baruch’s Masters in Financial Engineering.
Many have an unrealistic expectation or idealism of what a quant do, mostly from movies and books like “Wall Street”, “My Life as a Quant”, “Liar’s Poker”. What would you tell them?
I agree that, to me at least, “Wall Street” was not very realistic but both “My Life as a Quant” and “Liar’s Poker” were pretty realistic I thought. I take the attitude that most disappointments of this sort are not related to actual differences between expectation and reality but more to the individual’s view of reality. I always insisted to the quants that they ran the business and I meant it: Changing a model changes the way traders behave – something that is very hard to achieve as a trading manager. An alternative view might have been that these same quants were just producing models on trader request. There is some truth in both views but the first point of view is life-affirming, reinforcing the quant’s view of his own responsibility to the firm. The second point of view, taken to an extreme, absolves the quant of any such responsibility. One can easily see how two quants faced with the same reality can have very different levels of career satisfaction.
Many students have viewed MFE programs as a quick pathway to a “get rich quick” job on Wall Street. In fact, the number of universities opening up this type of programs has increased the past decade. This leads to a wide quality disparity among people coming out of these programs. What would you advise aspiring students who plan to join these MFE programs?
I would advise students to pay careful attention to the content of a master’s program. The prestige that comes from being admitted to one of the top schools will get you through the first year or two but not necessarily further. A master’s program offers the student a golden opportunity to build a solid basis for his future career; course content and quality of teaching are crucial.
What would be the essential skill/knowledge every financial engineer should have?
Every financial engineer, in addition to what is taught in a typical masters course, should be able to communicate effectively with non-specialists and should have a basic understanding of business and financial accounting.
What other job options should one look at outside of Wall Street where quantitative skills are appreciated?
I’m no expert. But based on the people I have seen hired as quants, bio-engineering and computational genomics are two areas in which highly quantitative graduates can find rewarding employment outside the financial industry.
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Baruch金融工程之数量金融大师Professor Jim Gatheral专访：有的放矢，瞄准华尔街
发布日期：2013-09-11 03:48 来源：北京大学国家发展研究院
Dr. Jim Gatheral于1983年取得剑桥大学理论物理博士学位，是闻名华尔街的数量金融大师，他的职业及研究经历涉及金融衍生品定价、风险控制及交易优化。在25年的职业生涯中，他曾于纽约，伦敦，东京等地从事第一线的交易工作。其中17年时间在美林银行（Bank of
America Merrill Lynch）担任董事总经理（Managing Director），负责定量分析研究。同时，从1998年至今，Jim Gatheral教授先后在纽约大学和纽约市立大学担任教职，从事金融工程领域教学工作。
Jim Gatheral教授目前的研究兴趣为：算法交易中的波动率模型和市场微结构模型。Gatheral教授同时也是世界范围内学术及业界交流会议中的常客。他的专著 The Volatility Surface: A Practitioner's Guide 是数量金融领域的经典之作。
2013年10月20日-26日，Dr. Jim Gatheral将在国家发展研究院举行金融衍生品专题讲座，与同学们一起探讨华尔街最火热的交易模型，分享最前沿的金融业界动态。为此，我们就专业知识储备、金融市场发展前景和职业导向对Dr. Jim Gatheral进行了专访。
April 11, 2013. Dr. Jim Gatheral of the Financial Engineering Master’s Program at Baruch College was named Presidential Professor at Baruch College, City University of New York.
Dr. Gatheral joined Baruch College in August 2010, after a distinguished career in finance, most recently as a Managing Director at Merrill Lynch, where he led the quantitative research group for 17 years. Before making his way to New York, Dr. Gatheral’s work spanned the trading and banking worlds, working out of London and Tokyo.
Dr. Gatheral, one of the top quants in the world and author of the best selling book “The Volatility Surface: A Practitioner’s Guide“, has strengthened the Baruch MFE Program, propelling it to be included among the top five such programs by the end of 2011. Jim’s impact on the Baruch MFE program has been significant and multi-faceted, leading directly to not only increasing the program’s prestige and standing, but also attracting even stronger students who have since won international competitions, including the Rotman International Trading Competition and the Metaquotes Automated Trading Championship.
Can you give us a recap on your education background and career?
My undergraduate degree was in Mathematics and Natural Philosophy at the University of Glasgow. My PhD was in Theoretical Physics at Cambridge University and my thesis title “Infrared Divergences in Perturbative QCD”. I joined Bank of America in London as a trainee loan officer in 1983 with the title of Assistant Cashier, receiving my first bonus of £400 in 1985. I started trading currency options at BofA in 1984 and subsequently joined Bankers Trust to trade interest rate derivatives in 1986. I ended up running currency options and then ran Capital Markets (effectively derivatives trading) in Tokyo in the early 1990s. I joined Merrill Lynch in 1993 as global head of equity derivatives trading and set up the quant team in 1996, retiring from the firm earlier this year.
Your research has recently moved into Market Microstructure. Tell us what is significant about this area that captures your interest?
What is really interesting about this area is that nothing is yet well understood, and the timing is perfect because we now have access to huge amounts of high frequency data. Obviously, with my physics background, I am particularly interested in the recent developments in econophysics. From the theoretical point of view, I think we are closer than ever to a good understanding of the process of price formation and from the practical point of view, simple physics-style models have already led to the development of more efficient algorithms that minimize transaction costs.
How do you connect Market Microstructure to Algo trading?
The way I like to define things, market microstructure is the theory underlying algorithm construction. That said, most algorithms are even now constructed without any theory – there is still a huge opportunity for quants to get involved.
What would be the area in quant finance that will experience most innovative research idea and product growth in the next few years?
If you will forgive me for talking my own book on this one, I think that the answers are market microstructure and volatility surface dynamics. More specifically, there is likely to be less emphasis on exotic derivatives in my view and more trading will take place on exchanges. This means in particular that we won’t need models to compute the prices of financial derivatives because pricing will be transparent. And if there is less inventory on dealer books, that means that options dealers will need to better understand and model the shapes and dynamics of volatility surfaces, just as option market makers attempt to do today. Up to now, models have been largely normative: We come up with some plausible process, derive some pricing formula, and fit the resulting model to market prices. In the future, models will have to have realistic dynamics, consistent with observation. Control of execution costs will also be critical and for that, a good understanding of market microstructure will be essential.
What would you advise people who want a career as a financial engineer?
My advice to such people is the same as my advice to anyone embarking on any career: Keep studying and learning, keep up with the latest research and find people to work with who will teach you something.
Where do you see a growth for people with quantitative background?
Obviously, my background is finance and I do think that there will be continued and increasing demand for people with quantitative backgrounds. I can foresee a time when it will be more or less impossible to get a trading job without a graduate qualification such as Baruch’s Masters in Financial Engineering.
Many have an unrealistic expectation or idealism of what a quant do, mostly from movies and books like “Wall Street”, “My Life as a Quant”, “Liar’s Poker”. What would you tell them?
I agree that, to me at least, “Wall Street” was not very realistic but both “My Life as a Quant” and “Liar’s Poker” were pretty realistic I thought. I take the attitude that most disappointments of this sort are not related to actual differences between expectation and reality but more to the individual’s view of reality. I always insisted to the quants that they ran the business and I meant it: Changing a model changes the way traders behave – something that is very hard to achieve as a trading manager. An alternative view might have been that these same quants were just producing models on trader request. There is some truth in both views but the first point of view is life-affirming, reinforcing the quant’s view of his own responsibility to the firm. The second point of view, taken to an extreme, absolves the quant of any such responsibility. One can easily see how two quants faced with the same reality can have very different levels of career satisfaction.
Many students have viewed MFE programs as a quick pathway to a “get rich quick” job on Wall Street. In fact, the number of universities opening up this type of programs has increased the past decade. This leads to a wide quality disparity among people coming out of these programs. What would you advise aspiring students who plan to join these MFE programs?
I would advise students to pay careful attention to the content of a master’s program. The prestige that comes from being admitted to one of the top schools will get you through the first year or two but not necessarily further. A master’s program offers the student a golden opportunity to build a solid basis for his future career; course content and quality of teaching are crucial.
What would be the essential skill/knowledge every financial engineer should have?
Every financial engineer, in addition to what is taught in a typical masters course, should be able to communicate effectively with non-specialists and should have a basic understanding of business and financial accounting.
What other job options should one look at outside of Wall Street where quantitative skills are appreciated?
I’m no expert. But based on the people I have seen hired as quants, bio-engineering and computational genomics are two areas in which highly quantitative graduates can find rewarding employment outside the financial industry.
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