Volatility forecasting: long memory, regime switching and heteroscedasticity. Issue 38 (15th August 2019)

• Record Type: Journal Article
• Authors: Ma, Feng; Lu, Xinjie; Yang, Ke; Zhang, Yaojie
• Is Part Of: Applied economics. Volume 51:Issue 38(2019)
• Page Start: 4151
• Page End: 4163
• Publication Date: 2019-08-15
• Subjects: Volatility forecasting -- realized volatility -- long memory -- regime switching -- heteroscedasticity; G15 -- C22; Economics -- Periodicals; 330
• DOI: 10.1080/00036846.2019.1589645
• Languages: English
• ISSNs: 0003-6846

Abstract

ABSTRACT: 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.