Topics in the Trading and Modelling of Volatility and other Risks

Master Course

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  • Fabio Trojani


  • Fabio Trojani


  • Bocconi University
  • Date Time Classroom
    03/15/2021 Online
    05/30/2021 Online


The course is an introduction to recent advances in the model-free modelling and trading of volatility and other risks – such as skewness risk – using suitable option portfolios. We will provide a systematic theory for replicating suitable payoffs that are interpretable as realized volatility or realized skewness and we will infer their prices in a model-free way from corresponding option portfolios in arbitrage- free markets. While these results provide the foundations to, e.g., well-known volatility and skew indeces used in the practice – such as the CBOE VIX and SKEW indeces – they offer more generally a powerful unifying methodology for defining tradable notions of implied volatility and for trading various nonlinear risks, including volatility and skewness, in a model-independent way. Therefore, they also deliver a powerful approach for measuring in real time the market price of these risks, for developing models predicting future changes in the prices or the level of these risks, or for predicting future returns of other assets, such as market index returns, using observable information on the price of volatility or skewness.

The course aims to provide students in a workshop-like style with key skills and competences, allowing them to soundly understand key methdological aspects and to reproduce basic stylized empirical evidence from recent practically-relevant research papers in finance. More broadly, the course hopes to provide students with useful experience and intuition that is typically gained after structuring and addressing a research project with a data-analytic component. Lectures provide methodological background and empirical context that students need to deepen, both with their reading of the selected material to the course and with their largely independent empirical work in groups. The empirical work for the course is supported by a user-friendly cloud environment, which is designed to make the relevant financial data and the preferred application package (R) for the empirical analysis easily accessible to students within the course in an integrated way.