Improving volatility forecasts of the Nikkei 225 stock index using a realized EGARCH model with realized and realized range-based volatilities

2025/02/04 by Chang, Yaming
Economics, Econometrics and Finance · #Econometrics (econ.EM) #FOS: Economics and business #Market Dynamics and Volatility

paper · pdf · doi:10.48550/arxiv.2502.02695

openalex publication_date 2025/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper applies the realized exponential generalized autoregressive conditional heteroskedasticity (REGARCH) model to analyze the Nikkei 225 index from 2010 to 2017, utilizing realized variance (RV) and realized range-based volatility (RRV) as high-frequency measures of volatility. The findings show that REGARCH models outperform standard GARCH family models in both in-sample fitting and out-of-sample forecasting, driven by the dynamic information embedded in high-frequency realized measures. Incorporating multiple realized measures within a joint REGARCH framework further enhances model performance. Notably, RRV demonstrates superior predictive power compared to RV, as evidenced by improvements in forecast accuracy metrics. Moreover, the forecasting results remain robust under both rolling-window and recursive evaluation schemes.

Related

• Forecasting realized volatility in the stock market: a path-dependent perspective
• Forecasting realized covariances using HAR-type models
• On options-driven realized volatility forecasting: Information gains via rough volatility model
• GNAR-HARX Models for Realised Volatility: Incorporating Exogenous Predictors and Network Effects
• Tail risk forecasting using Bayesian realized EGARCH models