(NY) Andrew Papanicolaou: PCA for Implied Volatility Surfaces

PCA for Implied Volatility Surfaces
A Talk by Andrew Papanicolaou

Tuesday, January 7, 2020

5:45 PM Registration
6:00 PM Seminar Begins
7:30 PM Reception

Abstract
Principal component analysis (PCA) is a useful tool when trying to uncover factor models from historical asset returns. For the implied volatilities of U.S. equities there is a PCA-based model with a principal eigenportfolio whose return time series lies close to that of an overarching market factor. Specifically, this market factor is the index resulting from the daily compounding of a weighted average of implied-volatility returns, with weights based on the options' open interest (OI). We analyze the singular values derived from the tensor structure of the implied volatilities of S&P500 constituents, and find evidence indicating that the OI-weighted index is one of at least two significant factors in this market.

Biography
Andrew Papanicolaou has been a professor at NYU Tandon since 2015. His PhD is in applied mathematics from Brown University, and he has been a lecturer at the ORFE Department at Princeton University and in the School of Mathematics & Statistics at the University of Sydney. He holds a MS in Financial Mathematics from the University of Southern California and BS in Mathematical Sciences from the University of California at Santa Barbara.

Acknowledgments
Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.