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Stock returns, quantile autocorrelation, and volatility forecasting

Yixiu Zhao, Vineet Upreti Orcid Logo, Yuzhi Cai Orcid Logo

International Review of Financial Analysis, Volume: 73, Start page: 101599

Swansea University Authors: Vineet Upreti Orcid Logo, Yuzhi Cai Orcid Logo

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DOI (Published version): 10.1016/j.irfa.2020.101599

Abstract

We examine stock return autocorrelation at various quantiles of the returns' distribution and use it to forecast stock return volatility. Our empirical results show that the strength of the autoregression varies across the quantiles of the returns' distribution in terms of both magnitude a...

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Published in: International Review of Financial Analysis
ISSN: 1057-5219
Published: Elsevier 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa55342
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Abstract: We examine stock return autocorrelation at various quantiles of the returns' distribution and use it to forecast stock return volatility. Our empirical results show that the strength of the autoregression varies across the quantiles of the returns' distribution in terms of both magnitude and persistence. Specifically, the autoregression order and magnitude of the coefficients is lower in the left tail in comparison with the right tail. Additionally, we show that the quantile autoregressive (QAR) framework proposed in this study improves out-of-sample volatility forecasting performance compared to the generalised autoregressive conditional heteroscedasticity (GARCH)-type models and other quantile-based models. We also observe greater outperformance in QAR estimates during periods of financial turmoil. Moreover, the QAR method also explains the stylized ‘leverage effect’ associated with asset returns in the presence of volatility asymmetry.
Keywords: Quantile autoregression; Stock returns; Volatility forecasting; Volatility asymmetry
College: Faculty of Humanities and Social Sciences
Start Page: 101599

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