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Journal article 1317 views

A comparative study of monotone quantile regression methods for financial returns

Yuzhi Cai Orcid Logo

International Journal of Theoretical and Applied Finance, Volume: 19, Issue: 3, Pages: 1650016 - [16 pages]

Swansea University Author: Yuzhi Cai Orcid Logo

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DOI (Published version): 10.1142/S0219024916500163

Abstract

Quantile regression methods have been used widely in finance to alleviate estimationproblems related to the impact of outliers and the fat-tailed error distribution of financialreturns. However, a potential problem with the conventional quantile regression methodis that the estimated conditional qua...

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Published in: International Journal of Theoretical and Applied Finance
Published: 2016
URI: https://cronfa.swan.ac.uk/Record/cronfa27421
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first_indexed 2016-04-27T01:14:50Z
last_indexed 2019-07-23T14:39:03Z
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2019-07-23T10:41:38.2416441</datestamp><bib-version>v2</bib-version><id>27421</id><entry>2016-04-26</entry><title>A comparative study of monotone quantile regression methods for financial returns</title><swanseaauthors><author><sid>eff7b8626ab4cc6428eef52516fda7d6</sid><ORCID>0000-0003-3509-9787</ORCID><firstname>Yuzhi</firstname><surname>Cai</surname><name>Yuzhi Cai</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2016-04-26</date><deptcode>BAF</deptcode><abstract>Quantile regression methods have been used widely in finance to alleviate estimationproblems related to the impact of outliers and the fat-tailed error distribution of financialreturns. However, a potential problem with the conventional quantile regression methodis that the estimated conditional quantiles may cross over, leading to a failure of theanalysis. It is noticed that the crossing over issues usually occur at high or low quantilelevels, which are the quantile levels of great interest when analyzing financial returns.Several methods have appeared in the literature to tackle this problem. This studycompares three methods, i.e. Cai &amp; Jiang, Bondell et al. and Schnabel &amp; Eilers, forestimating noncrossing conditional quantiles by using four financial return series. Wefound that all these methods provide similar quantiles at nonextreme quantile levels.However, at extreme quantile levels, the methods of Bondell et al. and Schnabel &amp; Eilersmay underestimate (overestimate) upper (lower) extreme quantiles, while that of Cai &amp;Jiang may overestimate (underestimate) upper (lower) extreme quantiles. All methodsprovide similar median forecasts.</abstract><type>Journal Article</type><journal>International Journal of Theoretical and Applied Finance</journal><volume>19</volume><journalNumber>3</journalNumber><paginationStart>1650016</paginationStart><paginationEnd>[16 pages]</paginationEnd><publisher/><keywords>Noncrossing quantiles; quantile regression; financial returns.</keywords><publishedDay>30</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2016</publishedYear><publishedDate>2016-03-30</publishedDate><doi>10.1142/S0219024916500163</doi><url/><notes/><college>COLLEGE NANME</college><department>Accounting and Finance</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BAF</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2019-07-23T10:41:38.2416441</lastEdited><Created>2016-04-26T11:43:51.1000506</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Accounting and Finance</level></path><authors><author><firstname>Yuzhi</firstname><surname>Cai</surname><orcid>0000-0003-3509-9787</orcid><order>1</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2019-07-23T10:41:38.2416441 v2 27421 2016-04-26 A comparative study of monotone quantile regression methods for financial returns eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2016-04-26 BAF Quantile regression methods have been used widely in finance to alleviate estimationproblems related to the impact of outliers and the fat-tailed error distribution of financialreturns. However, a potential problem with the conventional quantile regression methodis that the estimated conditional quantiles may cross over, leading to a failure of theanalysis. It is noticed that the crossing over issues usually occur at high or low quantilelevels, which are the quantile levels of great interest when analyzing financial returns.Several methods have appeared in the literature to tackle this problem. This studycompares three methods, i.e. Cai & Jiang, Bondell et al. and Schnabel & Eilers, forestimating noncrossing conditional quantiles by using four financial return series. Wefound that all these methods provide similar quantiles at nonextreme quantile levels.However, at extreme quantile levels, the methods of Bondell et al. and Schnabel & Eilersmay underestimate (overestimate) upper (lower) extreme quantiles, while that of Cai &Jiang may overestimate (underestimate) upper (lower) extreme quantiles. All methodsprovide similar median forecasts. Journal Article International Journal of Theoretical and Applied Finance 19 3 1650016 [16 pages] Noncrossing quantiles; quantile regression; financial returns. 30 3 2016 2016-03-30 10.1142/S0219024916500163 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2019-07-23T10:41:38.2416441 2016-04-26T11:43:51.1000506 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1
title A comparative study of monotone quantile regression methods for financial returns
spellingShingle A comparative study of monotone quantile regression methods for financial returns
Yuzhi Cai
title_short A comparative study of monotone quantile regression methods for financial returns
title_full A comparative study of monotone quantile regression methods for financial returns
title_fullStr A comparative study of monotone quantile regression methods for financial returns
title_full_unstemmed A comparative study of monotone quantile regression methods for financial returns
title_sort A comparative study of monotone quantile regression methods for financial returns
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
format Journal article
container_title International Journal of Theoretical and Applied Finance
container_volume 19
container_issue 3
container_start_page 1650016
publishDate 2016
institution Swansea University
doi_str_mv 10.1142/S0219024916500163
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 0
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description Quantile regression methods have been used widely in finance to alleviate estimationproblems related to the impact of outliers and the fat-tailed error distribution of financialreturns. However, a potential problem with the conventional quantile regression methodis that the estimated conditional quantiles may cross over, leading to a failure of theanalysis. It is noticed that the crossing over issues usually occur at high or low quantilelevels, which are the quantile levels of great interest when analyzing financial returns.Several methods have appeared in the literature to tackle this problem. This studycompares three methods, i.e. Cai & Jiang, Bondell et al. and Schnabel & Eilers, forestimating noncrossing conditional quantiles by using four financial return series. Wefound that all these methods provide similar quantiles at nonextreme quantile levels.However, at extreme quantile levels, the methods of Bondell et al. and Schnabel & Eilersmay underestimate (overestimate) upper (lower) extreme quantiles, while that of Cai &Jiang may overestimate (underestimate) upper (lower) extreme quantiles. All methodsprovide similar median forecasts.
published_date 2016-03-30T03:33:14Z
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score 11.013731

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