No Cover Image

Journal article 542 views 59 downloads

Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models / Dimos S. Kambouroudis, David G. McMillan, Katerina Tsakou

Journal of Futures Markets, Volume: 36, Issue: 12, Pages: 1127 - 1163

Swansea University Author: Katerina Tsakou

Check full text

DOI (Published version): 10.1002/fut.21783

Abstract

We investigate the information content of implied volatility forecasts for stock index return volatility. Using different autoregressive models, we examine whether implied volatility forecasts contain information for future volatility beyond that in GARCH and realized volatility models. Results show...

Full description

Published in: Journal of Futures Markets
ISSN: 02707314
Published: 2016
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa34904
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2017-08-12T03:54:12Z
last_indexed 2020-06-22T18:46:26Z
id cronfa34904
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2020-06-22T14:51:36.7649211</datestamp><bib-version>v2</bib-version><id>34904</id><entry>2017-08-11</entry><title>Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models</title><swanseaauthors><author><sid>a4f50625221ac95136b3ff39782f2733</sid><ORCID>0000-0003-1913-858X</ORCID><firstname>Katerina</firstname><surname>Tsakou</surname><name>Katerina Tsakou</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2017-08-11</date><deptcode>BAF</deptcode><abstract>We investigate the information content of implied volatility forecasts for stock index return volatility. Using different autoregressive models, we examine whether implied volatility forecasts contain information for future volatility beyond that in GARCH and realized volatility models. Results show implied volatility follows a predictable pattern and confirm the existence of a contemporaneous relationship between implied volatility and index returns. Individually, implied volatility performs worse than alternate forecasts, however, a model that combines an asymmetric GARCH model with implied and realized volatility through (asymmetric) ARMA models is preferred model for forecasting volatility. This evidence is further supported by consideration of value-at-risk.</abstract><type>Journal Article</type><journal>Journal of Futures Markets</journal><volume>36</volume><journalNumber>12</journalNumber><paginationStart>1127</paginationStart><paginationEnd>1163</paginationEnd><publisher/><issnPrint>02707314</issnPrint><keywords/><publishedDay>1</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2016</publishedYear><publishedDate>2016-12-01</publishedDate><doi>10.1002/fut.21783</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>2020-06-22T14:51:36.7649211</lastEdited><Created>2017-08-11T19:02:19.1047934</Created><path><level id="1">School of Management</level><level id="2">Accounting and Finance</level></path><authors><author><firstname>Dimos S.</firstname><surname>Kambouroudis</surname><order>1</order></author><author><firstname>David G.</firstname><surname>McMillan</surname><order>2</order></author><author><firstname>Katerina</firstname><surname>Tsakou</surname><orcid>0000-0003-1913-858X</orcid><order>3</order></author></authors><documents><document><filename>0034904-26092018164402.pdf</filename><originalFilename>paper_jfm.pdf</originalFilename><uploaded>2018-09-26T16:44:02.3030000</uploaded><type>Output</type><contentLength>598817</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><action/><embargoDate>2018-09-26T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2020-06-22T14:51:36.7649211 v2 34904 2017-08-11 Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models a4f50625221ac95136b3ff39782f2733 0000-0003-1913-858X Katerina Tsakou Katerina Tsakou true false 2017-08-11 BAF We investigate the information content of implied volatility forecasts for stock index return volatility. Using different autoregressive models, we examine whether implied volatility forecasts contain information for future volatility beyond that in GARCH and realized volatility models. Results show implied volatility follows a predictable pattern and confirm the existence of a contemporaneous relationship between implied volatility and index returns. Individually, implied volatility performs worse than alternate forecasts, however, a model that combines an asymmetric GARCH model with implied and realized volatility through (asymmetric) ARMA models is preferred model for forecasting volatility. This evidence is further supported by consideration of value-at-risk. Journal Article Journal of Futures Markets 36 12 1127 1163 02707314 1 12 2016 2016-12-01 10.1002/fut.21783 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2020-06-22T14:51:36.7649211 2017-08-11T19:02:19.1047934 School of Management Accounting and Finance Dimos S. Kambouroudis 1 David G. McMillan 2 Katerina Tsakou 0000-0003-1913-858X 3 0034904-26092018164402.pdf paper_jfm.pdf 2018-09-26T16:44:02.3030000 Output 598817 application/pdf Accepted Manuscript true 2018-09-26T00:00:00.0000000 true eng
title Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models
spellingShingle Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models
Katerina, Tsakou
title_short Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models
title_full Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models
title_fullStr Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models
title_full_unstemmed Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models
title_sort Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models
author_id_str_mv a4f50625221ac95136b3ff39782f2733
author_id_fullname_str_mv a4f50625221ac95136b3ff39782f2733_***_Katerina, Tsakou
author Katerina, Tsakou
author2 Dimos S. Kambouroudis
David G. McMillan
Katerina Tsakou
format Journal article
container_title Journal of Futures Markets
container_volume 36
container_issue 12
container_start_page 1127
publishDate 2016
institution Swansea University
issn 02707314
doi_str_mv 10.1002/fut.21783
college_str School of Management
hierarchytype
hierarchy_top_id schoolofmanagement
hierarchy_top_title School of Management
hierarchy_parent_id schoolofmanagement
hierarchy_parent_title School of Management
department_str Accounting and Finance{{{_:::_}}}School of Management{{{_:::_}}}Accounting and Finance
document_store_str 1
active_str 0
description We investigate the information content of implied volatility forecasts for stock index return volatility. Using different autoregressive models, we examine whether implied volatility forecasts contain information for future volatility beyond that in GARCH and realized volatility models. Results show implied volatility follows a predictable pattern and confirm the existence of a contemporaneous relationship between implied volatility and index returns. Individually, implied volatility performs worse than alternate forecasts, however, a model that combines an asymmetric GARCH model with implied and realized volatility through (asymmetric) ARMA models is preferred model for forecasting volatility. This evidence is further supported by consideration of value-at-risk.
published_date 2016-12-01T03:54:06Z
_version_ 1714377348709613568
score 10.830003