Wednesday, December 23, 2020

Best Webinar Awards, IV (SoFiE)

Now let's do the wonderful Society for Financial Econometrics online seminar.  It's a tie!  

The first winner is 

Rob Engle (NYU Stern), 


"Measuring and Hedging Geopolitical Risk,"

with S. Martins.  A fascinating "geopolitical risk" interpretation of the factor structure in "idiosyncratic" components of asset return volatilities.

Abstract:  Geopolitical events can impact volatilities of all assets, asset classes, sectors and countries. It is shown that innovations to volatilities are correlated across assets and therefore can be used to measure and hedge geopolitical risk. We introduce a definition of geopolitical risk which is based on volatility shocks to a wide range of financial market prices. To measure geopolitical risk, we propose a statistical model for the magnitude of the common volatility shocks. Accordingly, a test and estimation methods are developed and studied using both empirical and simulated data. We provide a novel explanation for why idiosyncratic volatilities comove based on a new way to formulate multiplicative factors. Finally, we propose a new criterion for portfolio optimality which is intended to reduce the exposure to geopolitical risk.

The second winner is 

Patrick Gagliardini (U Lugano), 


"Extracting Statistical Factors When Betas Are Time-Varying,"

with H. Ma.  Time-varying betas are a key route to time-varying financial network connectedness, near and dear to my heart.

Check out the videos here. 

This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is nonparametric regarding the way the loadings vary in time as functions of common shocks and individual characteristics. The number of active factors can also be time-varying as an effect of the changing macroeconomic environment. The method deploys Instrumental Variables (IV) which have full-rank covariation with the factor betas in the cross-section. It allows for a large dimension of the vector generating the conditioning information by machine learning techniques. In an empirical application, we infer the conditional factor space in the panel of monthly returns of individual stocks in the CRSP dataset between January 1971 and December 2017.

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