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The role of model bias in predicting volatility: evidence from the US equity markets

Yan Li (Southwest Jiaotong University, Chengdu, China)
Lian Luo (Southwest Jiaotong University, Chengdu, China)
Chao Liang (Southwest Jiaotong University, Chengdu, China)
Feng Ma (Southwest Jiaotong University, Chengdu, China)

China Finance Review International

ISSN: 2044-1398

Article publication date: 27 October 2020

Issue publication date: 6 February 2023




The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.


Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively.


The in-sample results reveal that the prediction model including the model bias can obtain bigger R2, and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models.


The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.



The authors acknowledge the support from the Natural Science Foundation of China [71671145, 71701170], the Humanities and Social Science Fund of the Ministry of Education [17YJC790105, 17XJCZH002], and Fundamental research funds for the central universities [682017WCX01, 2682018WXTD05, 30919013232].


Li, Y., Luo, L., Liang, C. and Ma, F. (2023), "The role of model bias in predicting volatility: evidence from the US equity markets", China Finance Review International, Vol. 13 No. 1, pp. 140-155.



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