Informative Option Portfolios in Unscented Kalman Filter Design for Affine Jump Diffusion Models
40 Pages Posted: 25 Feb 2020
Date Written: October 11, 2019
Option pricing models are tools for pricing and hedging derivatives. Good models are complex and the econometrician faces many design decisions when bringing them to the data. I show that strategically constructed low-dimensional filter designs outperform those that try to use all the available option data. I construct Unscented Kalman Filters around option portfolios that aggregate option data, and track changes in risk-neutral volatility and skewness. These low-dimensional filters perform equivalently to or better than standard approaches that treat full option panels. The performance advantage is greatest in empirically relevant settings: in models with strongly skewed jump components that are not driven by Brownian volatility.
Keywords: affine models, option pricing, filtering, unscented Kalman filter
JEL Classification: G13, C51, C53
Suggested Citation: Suggested Citation