Andrew Papanicolaou, New York University

PCA for Implied Volatility Surfaces
Date
Feb 19, 2020, 4:30 pm5:30 pm
Location
101 - Sherrerd Hall
Event Description

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.