Forecasting VIX using filtered historical simulation

Jiang, Y. and Lazar, E. ORCID: https://orcid.org/0000-0002-8761-0754 (2022) Forecasting VIX using filtered historical simulation. Journal of Financial Econometrics, 20 (4). pp. 665-680. ISSN 1479-8417 doi: 10.1093/jjfinec/nbaa041

Abstract/Summary

We propose a new VIX forecast method using GARCH models based on the filtered historical simulation put forward in Barone-Adesi et al. (2008). The flexible change of measure accommodates for non-normalities such as negative skewness and positive excess kurtosis. We present an application for four well-established volatility indices (VIX9D, VIX, VIX3M and VIX6M). Our results show that our proposed estimation method outperforms the Normal-VIX model of Hao and Zhang (2013) both in-sample and out-of-sample. Furthermore, the use of volatility indices reduces the computational burden significantly compared to the options based pricing method.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/93184
Item Type	Article
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting
Publisher	Oxford University Press
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