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

The role of model bias in predicting volatility: evidence from the US equity markets The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.Design/methodology/approachUnder 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.FindingsThe 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.Originality/valueThe author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Finance Review International Emerald Publishing

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

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References (50)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2044-1398
DOI
10.1108/cfri-04-2020-0037
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.Design/methodology/approachUnder 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.FindingsThe 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.Originality/valueThe author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.

Journal

China Finance Review InternationalEmerald Publishing

Published: Feb 6, 2023

Keywords: Realized volatility; Model bias; Volatility forecasting; Equity markets; C22; C52; C55

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