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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
2019 (English)In: THEORY AND APPLICATIONS OF TIME SERIES ANALYSIS / [ed] Valenzuela, O; Rojas, F; Pomares, H; Rojas, I, Springer, 2019, Vol. 2, p. 149-169, article id 177Conference 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
Springer, 2019. Vol. 2, p. 149-169, article id 177
Series
Contributions to Statistics, ISSN 1431-1968
Keywords [en]
Long Memory Conditional Heteroskedastic Model, Return Volatility.
National Category
Economics
Identifiers
URN: urn:nbn:se:bth-17211DOI: 10.1007/978-3-030-26036-1_11ISI: 000578472600011ISBN: 978-3-030-26036-1 (print)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: 2020-11-13Bibliographically approved

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Quoreshi, Shahiduzzaman

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