Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks*

Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts

Yue Qiu, Tian Xie, Jun Yu and Qiankun Zhou

Journal of Financial Econometrics, 2022, vol. 20, issue 1, 160-186

Abstract: The linkage among the realized volatilities of component stocks is important when modeling and forecasting the relevant index volatility. In this article, the linkage is measured via an extended Common Correlated Effects (CCEs) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common factors are extracted using the principal component analysis. Empirical studies show that realized volatility models exploiting the linkage effects lead to significantly better out-of-sample forecast performance, for example, an up to 32% increase in the pseudo R2. We also conduct various forecasting exercises on the linkage variables that compare conventional regression methods with popular machine learning techniques.

Keywords: volatility forecasting; heterogeneous autoregression; common correlated effect; factor analysis; random forest (search for similar items in EconPapers)
JEL-codes: C31 C32 G12 G17 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Working Paper: Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks (2019)
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