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Article: Hybrid Quantile Regression Estimation For Time Series Models With Conditional Heteroscedasticity

TitleHybrid Quantile Regression Estimation For Time Series Models With Conditional Heteroscedasticity
Authors
KeywordsBootstrap method
Conditional quantile
Generalized auto-regressive conditional heteroscedasticity
Non-linear time series
Quantile regression
Issue Date2018
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB
Citation
Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2018, v. 80 n. 5, p. 975-993 How to Cite?
AbstractEstimating conditional quantiles of financial time series is essential for risk management and many other financial applications. For time series models with conditional heteroscedasticity, although it is the generalized auto‐regressive conditional heteroscedastic (GARCH) model that has the greatest popularity, quantile regression for this model usually gives rise to non‐smooth non‐convex optimization which may hinder its practical feasibility. The paper proposes an easy‐to‐implement hybrid quantile regression estimation procedure for the GARCH model, where we overcome the intractability due to the square‐root form of the conditional quantile function by a simple transformation. The method takes advantage of the efficiency of the GARCH model in modelling the volatility globally as well as the flexibility of quantile regression in fitting quantiles at a specific level. The asymptotic distribution of the estimator is derived and is approximated by a novel mixed bootstrapping procedure. A portmanteau test is further constructed to check the adequacy of fitted conditional quantiles. The finite sample performance of the method is examined by simulation studies, and its advantages over existing methods are illustrated by an empirical application to value‐at‐risk forecasting.
Persistent Identifierhttp://hdl.handle.net/10722/272963
ISSN
2017 Impact Factor: 2.894
2015 SCImago Journal Rankings: 7.429

 

DC FieldValueLanguage
dc.contributor.authorZheng, Y-
dc.contributor.authorZhu, Q-
dc.contributor.authorLi, G-
dc.contributor.authorXiao, Z-
dc.date.accessioned2019-08-06T09:19:59Z-
dc.date.available2019-08-06T09:19:59Z-
dc.date.issued2018-
dc.identifier.citationJournal of the Royal Statistical Society. Series B: Statistical Methodology, 2018, v. 80 n. 5, p. 975-993-
dc.identifier.issn1369-7412-
dc.identifier.urihttp://hdl.handle.net/10722/272963-
dc.description.abstractEstimating conditional quantiles of financial time series is essential for risk management and many other financial applications. For time series models with conditional heteroscedasticity, although it is the generalized auto‐regressive conditional heteroscedastic (GARCH) model that has the greatest popularity, quantile regression for this model usually gives rise to non‐smooth non‐convex optimization which may hinder its practical feasibility. The paper proposes an easy‐to‐implement hybrid quantile regression estimation procedure for the GARCH model, where we overcome the intractability due to the square‐root form of the conditional quantile function by a simple transformation. The method takes advantage of the efficiency of the GARCH model in modelling the volatility globally as well as the flexibility of quantile regression in fitting quantiles at a specific level. The asymptotic distribution of the estimator is derived and is approximated by a novel mixed bootstrapping procedure. A portmanteau test is further constructed to check the adequacy of fitted conditional quantiles. The finite sample performance of the method is examined by simulation studies, and its advantages over existing methods are illustrated by an empirical application to value‐at‐risk forecasting.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB-
dc.relation.ispartofJournal of the Royal Statistical Society. Series B: Statistical Methodology-
dc.rightsThis is the peer reviewed version of the following article: Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2018, v. 80 n. 5, p. 975-993, which has been published in final form at https://doi.org/10.1111/rssb.12277. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectBootstrap method-
dc.subjectConditional quantile-
dc.subjectGeneralized auto-regressive conditional heteroscedasticity-
dc.subjectNon-linear time series-
dc.subjectQuantile regression-
dc.titleHybrid Quantile Regression Estimation For Time Series Models With Conditional Heteroscedasticity-
dc.typeArticle-
dc.identifier.emailLi, G: gdli@hku.hk-
dc.identifier.authorityLi, G=rp00738-
dc.description.naturepostprint-
dc.identifier.doi10.1111/rssb.12277-
dc.identifier.scopuseid_2-s2.0-85055447084-
dc.identifier.hkuros299668-
dc.identifier.volume80-
dc.identifier.issue5-
dc.identifier.spage975-
dc.identifier.epage993-
dc.publisher.placeUnited Kingdom-

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