33 Pages Posted: 31 Oct 2012 Last revised: 24 Jun 2016
Date Written: March 25, 2014
This paper uses information on VIX to improve the empirical performance of GARCH models for pricing options on the S&P 500. In pricing multiple cross-sections of options, the models' performance can clearly be improved by extracting daily spot volatilities from the series of VIX rather than by linking spot volatility with different dates by using the series of the underlying's returns. Moreover, in contrast to traditional returns-based maximum likelihood estimation (MLE), a joint MLE with returns and VIX improves option pricing performance, and for NGARCH, joint MLE can yield empirically almost the same out-of-sample option pricing performance as direct calibration does to in-sample options, but without costly computations. Finally, consistently with the existing research, this paper finds that non-affine models clearly outperform affine models.
Keywords: Option valuation, VIX, GARCH, Estimation
JEL Classification: G13
Suggested Citation: Suggested Citation
Kanniainen, Juho and Lin, Binghuan and Yang, Hanxue, Estimating and Using GARCH Models with VIX Data for Option Valuation (March 25, 2014). Available at SSRN: https://ssrn.com/abstract=2168078 or http://dx.doi.org/10.2139/ssrn.2168078
By Jianfeng Hu