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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
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URI: https://cronfa.swan.ac.uk/Record/cronfa55342
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first_indexed 2020-10-06T10:41:23Z
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spelling 2021-01-25T15:33:44.2885943 v2 55342 2020-10-06 Stock returns, quantile autocorrelation, and volatility forecasting 8f0fcae811cfbfabf93901185944c055 0000-0002-9803-7551 Vineet Upreti Vineet Upreti true false eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2020-10-06 BAF 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. Journal Article International Review of Financial Analysis 73 101599 Elsevier 1057-5219 Quantile autoregression; Stock returns; Volatility forecasting; Volatility asymmetry 1 1 2021 2021-01-01 10.1016/j.irfa.2020.101599 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2021-01-25T15:33:44.2885943 2020-10-06T11:36:29.2770276 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yixiu Zhao 1 Vineet Upreti 0000-0002-9803-7551 2 Yuzhi Cai 0000-0003-3509-9787 3 55342__18330__b2a12a8ac5fb4b7d9c5d739ab920f739.pdf AAM.pdf 2020-10-06T11:39:40.6660373 Output 1254425 application/pdf Accepted Manuscript true 2022-04-09T00:00:00.0000000 ©2020 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Stock returns, quantile autocorrelation, and volatility forecasting
spellingShingle Stock returns, quantile autocorrelation, and volatility forecasting
Vineet Upreti
Yuzhi Cai
title_short Stock returns, quantile autocorrelation, and volatility forecasting
title_full Stock returns, quantile autocorrelation, and volatility forecasting
title_fullStr Stock returns, quantile autocorrelation, and volatility forecasting
title_full_unstemmed Stock returns, quantile autocorrelation, and volatility forecasting
title_sort Stock returns, quantile autocorrelation, and volatility forecasting
author_id_str_mv 8f0fcae811cfbfabf93901185944c055
eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv 8f0fcae811cfbfabf93901185944c055_***_Vineet Upreti
eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Vineet Upreti
Yuzhi Cai
author2 Yixiu Zhao
Vineet Upreti
Yuzhi Cai
format Journal article
container_title International Review of Financial Analysis
container_volume 73
container_start_page 101599
publishDate 2021
institution Swansea University
issn 1057-5219
doi_str_mv 10.1016/j.irfa.2020.101599
publisher Elsevier
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
active_str 0
description 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.
published_date 2021-01-01T04:09:29Z
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score 11.017797

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