Volatility forecasting: long memory, regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We...

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Bibliographic Details
Published in:Applied economics 2019-08, Vol.51 (38), p.4151-4163
Main Authors: Ma, Feng, Lu, Xinjie, Yang, Ke, Zhang, Yaojie
Format: Article
Language:English
Subjects:
Clustering
Economic analysis
Economic models
Economic theory
Forecasting
heteroscedasticity
long memory
Memory
Power
realized volatility
regime switching
Robustness
Securities markets
Volatility
Volatility forecasting
Windows
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Summary:In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.
ISSN:0003-6846
1466-4283
DOI:10.1080/00036846.2019.1589645