Forecasting VIX Using Filtered Historical Simulation

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AbstractWe propose a new VIX forecast method using Generalized Autoregressive Conditional Heteroscedasticity models based on the filtered historical simulation put forward in Barone-Adesi, Engle, and Mancini (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.