14.08.2024Visiting Faculty: Andrea Vedolin

Andrea Vedolin is a Professor of Finance at Boston University. In July and August 2024, we had the pleasure of her staying at our department for a month as a visiting professor. We seized the opportunity to ask her a few questions about what drives her interest in financial markets.

Many of your papers touch on uncertainty—in interest rates, in credit markets, in exchange rates. What draws you to this theme?

Uncertainty is ubiquitous. We do not know whether the stocks we bought are going to increase over time, what the weather is going to be next week or what the health of our loved ones is going to be in the future. Thinking about uncertainty and how human beings deal with it, therefore seems very natural to me. While it is of course impossible to quantify uncertainty in our life decisions, there are ways to measure such uncertainty in financial markets.

One example relates to what happened recently, when the so-called fear gauge index in the US (VIX) increased to levels not seen since the Great Financial Crisis in 2008. The question now is whether this increase in “fear” or “uncertainty” really reflects some fundamental panic in markets and how and whether it will spill over to other markets as well. The interesting thing is that despite the fact that uncertainty seems so natural, its effect on financial markets is still poorly understood.

Your research crosses several domains—asset pricing, international finance, monetary policy. How has this interdisciplinary approach shaped your insights? Is finance research becoming more interdisciplinary?

I think indeed that the finance profession has become more “accepting” of ideas from other fields. One recent example is behavioral finance or the effect of psychology in finance and economics. My sense is that ten years ago, it would have been much harder to publish experimental papers in a mainstream finance journal. These days, I see lots of papers in behavioral finance in the top journals. One reason for this, surely, has been the fact that now we have much better and more data available.

Financial Markets are (increasingly) complex, and models are limited. How do modelling limitations impact asset pricing, investor behavior or market efficiency?

Benchmark models in economics and finance assume that agents understand the world they live in and all its complexities. That seems a very strong assumption to me. Related to my previous point, lots of research shows that people do not react according to this paradigm. While it may be reasonable to assume that households do not always react in the most rational way, we also see that very sophisticated agents such as hedge funds or intermediaries often deviate from these principles of rationality. In my research, I try to study how small deviations from this rationality can affect prices in markets. The main premise of my research is that agents may use simplified versions of the very complex models that drive economic fundamentals such as inflation or economic growth.

Does development of AI and increasing computing power allow for more complex modeling and better models and predictions? 

I think there are many exciting areas where AI will have (and already has) a huge impact, such as in medicine. I am less excited about its effect in finance. Markets already seem highly efficient and to some extent the high-frequency arms race that took place the past ten years has already shown that at the end of the day, it will be only a handful of firms who will profit the most from such development.

You have worked as a researcher in the US and in Europe / Switzerland: How does academic life and culture differ?

I think over the past 10 years, academic life has become more similar between Europe and the US, since it has become more common for European PhDs to start their careers in the US and vice versa, lots of people who have made a career in the US returned to Europe. I personally do not feel that there are very big differences anymore.

Visit Andrea Vedolin’s Webpage here