I am mostly focused on empirical modelling of financial instruments such as options, bonds, and so forth, but I take a particular twist on this approach whereby I try to think of unusual data sources that will give me a different picture about the instruments that I am looking at. The objective is to bring in additional information that conflicts with the models people typically use, to create a tension that helps highlight deficiencies of the existing models, with a view to ultimately improving these models. In some of my older work, I brought together joint information from options and underlying asset returns to work on derivatives pricing; currently, I am looking in tandem at information from the yield curve and survey forecasts of inflation and interest rates. I take these seemingly unrelated sources and models and see what they tell us about, for example, the relationship between financial markets and the macro environment.
I am teaching an empirical option pricing course where I draw on my work and other recent advances in models capturing the joint dynamics of assets and their associated derivatives. These models allow to quantify sources of risk affecting an asset and to measure how each of these risks is priced. The course highlights empirical challenges with option pricing, and reviews various techniques and when and how to use them to arrive at good pricing models. This happens not only to be useful for practical option pricing but also for the development of realistic general equilibrium models.
In a lot of my research, I try and argue that a lot of puzzling features of the data start to make sense if you take into account crash risk into your modelling. This particular paper looks at returns from currency speculation. This is a popular topic because the returns from currency speculation are huge; what I am trying to understand is whether crash risk is responsible for these high returns, i.e. if the returns are compensation for rare but enormous risk that investors engaging in carry trades are facing.
We develop an empirical model of exchange rate dynamics that does a good job at capturing important properties in the data–such as stochastic volatility, price jumps, and volatility jumps–and estimate it using a joint dataset of currency returns and short-term at-the-money-implied volatilities extracted from currency options. We use twenty-four years of daily data on four spot exchange rates. Our estimation methodology provides estimates of the conditional distribution of currency returns, as well as estimates of realised shocks. This feature allows us to link big shocks, or jumps, to important macro-finance events that appear to trigger these shocks. This illuminates the potential economic channels that are responsible for crash risk in currencies and this is a first step towards understanding risk premia in currency markets.
Yes, very much so in 2008. As mentioned, the big theme of my research is the importance of jump risk and how you cannot explain prices if you do not take the risk of crashes into account. At the beginning of my career, the economy was doing great, the crash of 1987 was a distant memory and nobody cared about this anymore.
What happened during the crisis highlights the importance of jump risk. Naturally, statistical models do not give you structural interpretations, but at least you know that jump risk is reflected in option prices. At a very basic level, if you are a money manager, even if you are in a benign environment, if you think about jump risk and how it can impact your portfolio, this can make a big difference; Risk management is one of the key applications of this.
Teaching an elective course like this is an enticing opportunity to talk about your research and focus on things that you really care about. This is quite unusual, even at the doctoral level. I have been researching this area for over ten years and this provides a great opportunity to organise my thoughts on the topic and present them in a unified way. A lot of people have worked in the area and when we meet for paper presentations, it is too easy to focus on where we disagree with one another; this provides a superb occasion to put quarrels aside, step back, and review what we, as a profession, have learnt on the topic. But you do not do this just for yourself: a course like this one, because it is an elective, is a unique opportunity to train people who are enthusiastic about the subject.
To be honest, I did not know what to expect because a PhD programme that is geared towards executives is quite an unusual concept. People in the class are very different from the executive students that I am used to teaching. They are on top of things, they know what I am talking about and are well trained, they are interested in the subject-matter. Moreover, in contrast to regular PhD students, they bring a practical and valuable perspective on the discussed topics. It has been a rewarding experience.
You have to go with your heart and pick the topic that is really interesting to you. Doing research on a topic that really matters to you is very hard as it is. You have to be really committed to your topic, stick to your guns and just do it, because many things will fail before you get to the right spot where everything comes together. That’s general advice; now if you ask me about more specific and personal topics, I think the interplay between economics and finance is a very interesting field in which there is a lot of interesting work to be done: how financial frictions affect the economy, what you can learn about the macro-economy looking at financial prices, things like that I think are very important.