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Conditional Heteroskedasticity in Long Memory Model ‘FIMACH’ for Return Volatilities in Equity Markets
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics. Blekinge Tekniska Högskola.ORCID iD: 0000-0002-7277-9151
Swansea University, GBR.ORCID iD: 0000-0002-6342-8309
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper incorporates conditional heteroscedasticity properties in the long memory model and applies the model on squared returns of BRICS (Brazil, Russia, India, China, and South Africa), UK and USA equity markets to capture the volatility of stock return. The conditional first- and second-order moments are provided. The CLS, FGLS and QML are discussed and 2SQML estimator is proposed. The simulation study suggests that the proposed 2SQML estimator performs better than the other three estimators. Both in simulation and empirical studies, we find that the proposed model FIMACH outperforms FIGARCH in terms of eliminating serial correlations.

Place, publisher, year, edition, pages
2018. Vol. 2, p. 825-840, article id 177
Keywords [en]
Long Memory Conditional Heteroskedastic Model, Return Volatility.
National Category
Economics
Identifiers
URN: urn:nbn:se:bth-17211ISBN: 9788417293574 OAI: oai:DiVA.org:bth-17211DiVA, id: diva2:1260316
Conference
International Conference on Time Series and Forecasting, ITISE 2018,Granada
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-02Bibliographically approved

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fulltext(687 kB)10 downloads
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Authority records BETA

Quoreshi, Shahiduzzaman

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1516171819202118 of 28
CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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  • Other locale
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Output format
  • html
  • text
  • asciidoc
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