TITLE:
Forecasting Volatility Based on a New Combined HAR-Type Model with Long Memory and Switching Regime: Empirical Evidence from Equity Realized Volatility
AUTHORS:
Yirong Huang, Zhonglin Wan, Hongyan Li, Yi Luo
KEYWORDS:
Long Memory, Realized Volatility, Autoregressive Model, Forecast, Equity Market
JOURNAL NAME:
Journal of Mathematical Finance,
Vol.14 No.1,
February
27,
2024
ABSTRACT: 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 evaluation 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.