I do research in financial econometrics, the intersection between financial data and econometric methods.
Generally speaking, developing estimation and testing methods for continuous time models of asset pricing–whether at high or low frequency, and whether the data is on the underlying assets or on options or other derivatives on these assets–has been the common theme in my work.
Over the past few years, I have focused primarily on high frequency data issues, an area in which the literature has picked up significantly in recent years owing to the development of new research methods and the availability of massive amounts of data from exchanges and over-the-counter market players.
Some of the questions that have been investigated include how and why people trade and the resulting properties of the price processes that emerge, including whether they have jumps or not, what kinds of jumps, etc.
Such questions could not be addressed when we only had access to daily or lower frequency data; at the same time, tackling these questions requires that we bring in some tools from probability theory as well as develop new techniques, which has kept those of us working in this area rather busy.
Indeed, and the course was a good opportunity to expose students to different themes that are relevant when you do research on jumps. Rather than delve deeply into a single theme, I decided to give students an overview of different issues, some of which are of an economic nature, some of which concern methodological developments in financial econometrics. I tried to achieve a balance, taking into account the fact that many students were seeking ideas for their dissertations.
Among the questions we discussed, one of them was quantifying and testing the jump activity of an asset price process, another one was how to separate jumps from “normal” volatility for model calibration, a third one was how to develop models for jumps that are able to account for the type of dynamics observed during the recent financial crisis, a final field of investigation was to look at the consequences for risk management or optimal portfolio choice of the presence of jumps.
Yes, we tried to model the propagation of adverse shocks to stock markets across the world, noting that a jump in one region of the world seemed to increase the likelihood of a further jump in another region of the world. Our model of asset returns includes a drift component, a volatility component and mutually exciting jumps known as Hawkes processes. In the model, a jump in one region of the world (or a segment of the market) increases the intensity of jumps occurring both in the same region (self excitation) as well as other regions (cross excitation). Interestingly, it generates the type of jump clustering that is observed empirically. Empirically, we find evidence of self excitation in different world markets. We also find that US jumps tend to get reflected quickly in most other markets, while statistical evidence for the reverse transmission is much less pronounced.
Rene Garcia and I have been friends for over twenty years; I was intrigued when he told me he was joining EDHEC Business School and was similarly intrigued when he asked me to be part of the affiliated faculty.
Research-wise, I was much impressed with what EDHEC-Risk Institute had been able to accomplish in less than ten years’ time. The concept of a doctoral program opened to executives was novel and very interesting and I wanted to find out what type of profiles students would have. I thus accepted Professor Garcia’s invitation and am glad I had this opportunity to teach in the program.
I should first note that this was an elective course, so there is an obvious sample selection bias relative to the core courses that all students must take. The students in the course are different from those I usually encounter in a standard PhD program: they are more diverse and have been away from school for a longer period; at the same time, their work experience gives them a different perspective on the material; and typically they are more interested in the why of things and less in the how, which is a useful perspective to keep in mind when you work on material of a technical nature. It was also useful for me to confront people who have been in the industry for a while and gauge their reactions to new ideas and material.
While Ben Bernanke was Chair of the economics department in the 1990s, Princeton came to the realization that finance was becoming an increasingly important area of economics. So the university, not having a business school, came up with the concept of setting up an interdisciplinary centre to conduct research and teaching in finance. We created the Bendheim Center for Finance. Our faculty consists of 31 professors whose main appointment is in a University department. Our undergraduate program in finance has students majoring in fifteen different fields, from mathematics to history, from computer science to East-Asian studies; their research interests are very different from that of the typical finance student. At the graduate level, we supervise PhD students and also offer a professional Master in Finance program, which is very successful with over 700 candidates vying for the 30 seats offered.
What is important is to find a research topic that is fundamentally relevant, original, and doable. I do not have a magic recipe for this, but my advice is to think critically about potential topics in that order. If you have in mind something that is relevant, you should first check whether it is original and then probably find a way to make it doable, especially if you are technically inclined.
The financial crisis has been terrible in many ways, but one of its few positive aspects has been to make financial research even more relevant to the public at large; more topics have come to the forefront thanks to the crisis that are both very important from an economic standpoint and very new from a methodological one.