Forecasting Volatility Based on a New Combined HAR-Type Model with Long Memory and Switching Regime: Empirical Evidence from Equity Realized Volatility — Oak Academic Publishing
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Forecasting Volatility Based on a New Combined HAR-Type Model with Long Memory and Switching Regime: Empirical Evidence from Equity Realized Volatility
School of Business, Sun Yat-sen University, Guangzhou, China
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Department of Finance and Economics, Dongguan Polytechnic, Dongguan, China
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School of Computer and Information, City College of Dongguan University of Technology, Dongguan, China
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Management College, Guangdong Polytechnic Normal University, Guangzhou, China
1 School of Business, Sun Yat-sen University, Guangzhou, China
2 Department of Finance and Economics, Dongguan Polytechnic, Dongguan, China
3 School of Computer and Information, City College of Dongguan University of Technology, Dongguan, China
4 Management College, Guangdong Polytechnic Normal University, Guangzhou, China
This paper proposes a new combined model accounting for short memory, long memory, heterogeneity, and switching regime to model realized volatility and forecast future volatility. We apply daily realized volatility series of SPX to estimate volatility model parameters of in-sample and full-sample, and forecast future daily out-of-sample volatility. The model estimated results show the significant impact of long memory, switching regime, heterogeneity and jump component. The results of out-of-sample volatility forecast evaluati on indicate that MS-LM-HAR outperforms the other fifteen models based on the evaluating method of loss function and MCS. Our findings suggest that incorporating the property of long memory and switching regime into HAR-type models can significantly increase the forecast performance of realized volatility models.
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