Journal article 1280 views 297 downloads
Stock returns, quantile autocorrelation, and volatility forecasting
International Review of Financial Analysis, Volume: 73, Start page: 101599
Swansea University Authors: Vineet Upreti , Yuzhi Cai
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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...
Published in: | International Review of Financial Analysis |
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ISSN: | 1057-5219 |
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Elsevier
2021
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URI: | https://cronfa.swan.ac.uk/Record/cronfa55342 |
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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 CBAE 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 Management School COLLEGE CODE CBAE 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 |
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8f0fcae811cfbfabf93901185944c055 eff7b8626ab4cc6428eef52516fda7d6 |
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8f0fcae811cfbfabf93901185944c055_***_Vineet Upreti eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai |
author |
Vineet Upreti Yuzhi Cai |
author2 |
Yixiu Zhao Vineet Upreti Yuzhi Cai |
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International Review of Financial Analysis |
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73 |
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101599 |
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2021 |
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Swansea University |
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10.1016/j.irfa.2020.101599 |
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Elsevier |
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Faculty of Humanities and Social Sciences |
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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 |
2021-01-01T14:00:48Z |
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1821323715105783808 |
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11.048042 |