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

Yaming Chang

arXiv 4 Feb 2025 · Econometrics

arXiv:2502.02695 · PDF · Extracted main text

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.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
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3Liu, L.Y., Patton, A.J., Sheppard, K (2015) Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes0.64422100%
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7Hansen, P.R., Huang, Z., Shek, H.H (2012) Realized GARCH: a joint model for returns and realized measures of volatility0.64422100%
8Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A., Shephard, N (2011) Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and n…0.64422100%
9Ding, Z., Granger, C.W. and Engle, R.F (1993) A long memory property of stock market returns and a new model0.40511100%
10Engle, R.F (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation0.40511100%

Showing the top 10 of 31 scored citations.