Long range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting
Author | Abuzayed B. |
Author | Al-Fayoumi N. |
Author | Charfeddine L. |
Available date | 2019-10-06T09:38:33Z |
Publication Date | 2018 |
Publication Name | Applied Economics |
Resource | Scopus |
ISSN | 0003-6846 |
Abstract | This study evaluates the sector risk of the Qatar Stock Exchange (QSE), a recently upgraded emerging stock market, using value-at-risk models for the 7 January 2007–18 October 2015 period. After providing evidence for true long memory in volatility using the log-likelihood profile test of Qu and splitting the sample and dth differentiation tests of Shimotsu, we compare the FIGARCH, HYGARCH and FIAPARCH models under normal, Student-t and skewed-t innovation distributions based on in and out-of-sample VaR forecasts. The empirical results show that the skewed Student-t FIGARCH model generates the most accurate prediction of one-day-VaR forecasts. The policy implications for portfolio managers are also discussed. 2017 Informa UK Limited, trading as Taylor & Francis Group. |
Language | en |
Publisher | Routledge |
Subject | long memory Sector analysis true versus spurious VaR volatility modelling |
Type | Article |
Pagination | 2569-2599 |
Issue Number | 23 |
Volume Number | 50 |
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Finance & Economics [400 items ]