Alfeus, M. and Nikitopoulos, C.S. (2022) Forecasting Volatility in Commodity Markets with Long-Memory Models. Journal of Commodity Markets, 28, Article ID: 100248. https://doi.org/10.1016/j.jcomm.2022.100248

has been cited by the following article:

• 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.