Artikel
Symmetric and Asymmetric Volatility Clustering Via GARCH Family Models: An Evidence from Religion Dominant Countries
Volatility clustering and asymmetry are considered as an essential element in time series data analysis for portfolio managers. This study is conducted to analyze the volatility clustering and asymmetry occurrence by employing different GARCH models. Data is collected from 11 Religion Dominant Countries (RDCs) based on daily stock returns from 2011 to 2017. The findings of the study show that volatility clustering increases the asymmetric comportment of daily stock market returns. We estimated the analytical competence of GARCH models and found that GJR-GARCH and EGARCH executed better results than GARCH (p, q) in RDCs stock markets. It also shows that GJR-GARCH and EGAECH explain the asymmetric behavior along with an accurate assessment of volatility clustering for the selected 11 RDCs stock markets. This study helps managers, investors, and corporations to make investment-related decisions.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Paradigms ; ISSN: 2410-0854 ; Volume: 13 ; Year: 2019 ; Issue: 1 ; Pages: 20-25 ; Lahore: University of Central Punjab
- Klassifikation
-
Wirtschaft
Information and Market Efficiency; Event Studies; Insider Trading
- Thema
-
Volatility Clustering
Religion Dominant Countries
Market Returns
Asymmetric Behavior
GARCH
GJR-GARCH
EGARCH
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Khan, Muhammad Salman
Khan, Kanwal Iqbal
Mahmood, Shahid
Sheeraz, Muhammad
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Central Punjab
- (wo)
-
Lahore
- (wann)
-
2019
- DOI
-
doi:10.24312/1900148130104
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Khan, Muhammad Salman
- Khan, Kanwal Iqbal
- Mahmood, Shahid
- Sheeraz, Muhammad
- University of Central Punjab
Entstanden
- 2019
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