Testing the linkages of Arab stock markets: a multivariate GARCH approach

Shahateet, Mohammed Issa; Shrydeh, Najib; Mohammad, Suleiman Jamal

doi:10.21511/imfi.16(4).2019.17

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…To solve this problem, Bollerslev (1987) and Baillie and Bollerslev (1989) used Student's t-distribution. The accuracy, usability, forecasting performance, and other characteristics of symmetric and asymmetric GARCH models have been examined in a number of studies, including those by Campbell and Hentschel (1992), Engle (1982), Shahateet (2019), Alberg et al (2008), Gökbulut and Pekkaya (2014), Maqsood et al (2013), (French et al 1987) and (Lee (2017).…”

Section: Introductionmentioning

confidence: 99%

Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis

Chalissery¹,

Anagreh

T.³

et al. 2022

JRFM

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The past few years have witnessed renewed interest in modelling and forecasting asymmetry in financial time series using a variety of approaches. The most intriguing of these strategies is the “asymmetric” or “leverage” volatility model. This study aims to conduct a review of asymmetric GARCH models using bibliometric analysis to identify their key intellectual foundations and evolution, and offers thematic and methodological recommendations for future research to advance the domain. Bibliometric analysis was used to identify patterns in and perform descriptive analysis of articles, including citation, co-authorship, bibliographic coupling, and co-occurrence analysis. The study located 856 research papers from the Scopus database between 1992 and 2021 using key phrase and reference search methods. Publication trends, most influential authors, leading countries, and top journals are described, along with a systematic review of highly cited articles. The study summarises the development, application, and performance evaluation of asymmetric GARCH models, which will help researchers and academicians significantly contribute to this literature by addressing gaps.

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Section: Introductionmentioning

confidence: 99%

Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis

Chalissery¹,

Anagreh

T.³

et al. 2022

JRFM

View full text Add to dashboard Cite

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Financial connectedness of GCC emerging stock markets

Hung

2021

Eurasian Econ Rev

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The Co-Movement between Emerging Stock Markets Using DCC-GARCH Model: Evidence from GCC and Amman Stock Exchange

Matar

2023

Ann. Finan. Econ.

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This study examines the existence of interrelationships among GCC and Jordanian stock markets. The study applied weekly data for stock price indices for the Amman Exchange (ASE) and Gulf Cooperation Council (GCC) countries, namely, Saudi Arabia, Qatar, Bahrain, Kuwait, Oman and the United Arab Emirates. In this analysis, weekly market returns were employed to avoid the overlapping of recognized holidays and trading hours in the GCC and the ASE. During the global financial crisis and the COVID-19 pandemic crisis, the sample period spanned from January 7, 2009 to December 30, 2020 for overall (626) weekly observations The main contribution of this study is to shed light on the dynamics of cross-market associations, particularly in the context of emerging economies can be concluded as follows: First, this study is a forefront study that tested the linkages between GCC and Jordanian Markets. Second, the period of this study covered various crises such as the global financial crisis, and COVID-19. Third, the empirical study of multivariate integration, which considers both pairwise interactions and the overall integration of different stock markets by utilizing cutting-edge econometric methods like VECM and DCC-GARCH. As a result, an examination of ASE’s weekly stock price index was conducted. The findings show that there is causality and correlation among stock market indices. Three unidirectional causality relationships running from KUW, OMAI, and UAEI to ASEI. Moreover, the results revealed two bidirectional causality relationships, one between ASEI and BAHI and the other between ASEI and UAEI. The conditional correlations appear to be generally negative, implying that higher ASE volatility causes worse GCC stock returns. Saudi Arabia has the highest conditional connections, followed by Bahrain, Qatar and the United Arab Emirates, while Kuwait and Oman’s stock markets are the least connected with the ASE among their peers. Since financial indices are essential for investors, portfolio managers, risk management and fiscal and monetary policy, the findings of this study may be interest of many parties of them, especially for investors, policymakers and governments.

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Testing the linkages of Arab stock markets: a multivariate GARCH approach

Cited by 4 publications

References 33 publications

Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis

Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis

Financial connectedness of GCC emerging stock markets

The Co-Movement between Emerging Stock Markets Using DCC-GARCH Model: Evidence from GCC and Amman Stock Exchange

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