Modelling and forecasting volatility with high-frequency and VIX information: a component realized EGARCH model with VIX

Xinyu Wu, Michelle Xia and Xindan Li

Applied Economics, 2023, vol. 55, issue 20, 2273-2291

Abstract: This paper studies the joint use of high-frequency and VIX information to model and forecast volatility. Our framework relies on an extension of the realized EGARCH (REGARCH) model, namely the component REGARCH model with VIX (hereafter REGARCH(C)-VIX). The REGARCH(C)-VIX model facilitates exploitation of the high-frequency and VIX information through the inclusion of realized measure and VIX for modelling and forecasting volatility. Moreover, the model features a component volatility structure, which has the ability to capture the long memory volatility. An empirical investigation with the S&P 500 index shows that the REGARCH(C)-VIX model outperforms a variety of competing models in both empirical fit and out-of-sample volatility forecasting. Our findings provide strong evidence for including the high-frequency and VIX information as well as the component volatility structure to model and forecast volatility.

Date: 2023
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DOI: 10.1080/00036846.2022.2102570

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