An investigation of stock market volatility: evidence from Dubai financial market

Salameh, Hussein; Al-Zu’bi, Bashar

doi:10.1108/jeas-04-2017-0020

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Salameh and Alzubi (2018) investigated Dubai stock market for volatility spillover with the UK and the USA from January 2014 to December 2015 using ARCH and GARCH models. The analysis revealed that Dubai Financial Market Index was affected by its own disturbances and from S&P500 but not from FTSE.…”

Section: Review Of Literaturementioning

confidence: 99%

Volatility in frontier markets: a Multivariate GARCH analysis

Seth

Singhania²

2019

JAMR

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Purpose The purpose of this paper is to analyze the existence of volatility spillover effect in frontier markets. This study also examines whether any linkages exist among these markets or not. Design/methodology/approach Monthly data of regional frontier markets, from 2009 to 2016, are analyzed using Multivariate GARCH (BEKK and Dynamic Conditional Correlation (DCC)) models. Findings The result of cointegration test shows that the sample frontier markets are not linked in long run, and Granger causality test reveals that the markets under consideration do not cause each other even in the short run. BEKK test says that the effect of the arrival of shock from the own market does not last for longer, whereas shock from other markets lasts with the stronger persistence, and according to DCC test, the volatility spillover exists for all the markets. Practical implications The results of present study suggest that the frontier markets are not cointegrated in the long run as well as in the short run, which opens the doors for long-term investments in these markets in future, which may lead to decent returns. Long-term investors may draw the benefits from including the financial assets in their portfolios from these non-integrated frontier markets; nevertheless, they have to consider and implement diversification and hedging strategies during the period of financial turmoil, so as to protect themselves against economic and financial distress. Originality/value Significant work has been done on developed, developing and emerging markets but frontier markets are not explored much so far. This paper is an attempt to see the status of frontier stock markets as potential financial markets for diversification benefits.

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Section: Review Of Literaturementioning

confidence: 99%

Volatility in frontier markets: a Multivariate GARCH analysis

Seth

Singhania²

2019

JAMR

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“…The outcomes also showed that the unpredictability of FSTE do not underwrite to the volatility of DFM, while S&P-500 pay to the volatility of DFM which means that outside shocks can influence the volatility of DFM. In addition, APX affects Dubai Financial Market Index (DFM) [10].…”

Section: Review Of Literaturementioning

confidence: 99%

Stochastic Pattern of Major Indices of Bombay Stock Exchange

Dadhich¹,

Chouhan²,

Adholiya³

2019

IJRTE

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It is interesting to get inside and draw a meaningful inference by studying the movement of various stock indices. Portfolio managers, analysts, and investors are very keen to know about the technical pattern of indices. They consider the stock market is one of the economic barometers or market indicators of an economy. Indian financial market has undergone radical and vital change during the past few years. The purpose of this study is to check stochastic movements in selected indices and to signify nexus and interdependency among one another by the virtue of econometric analysis. The study comprises of daily closing value from 1st April 2014-1st April 2018, including major indices i.e. S&P-BSE 100; S&P-BSE-200, S&P BSE-500, S&P-BSE:Large cap, S&P-BSE:Mid-cap, S&P-BSE:small-cap, and BSE-SENSEX. Moreover, typical econometrics tool Augmented Dickey-Fuller Test, Granger Causality Test, and Johansen Co-integration Test were implemented to conclude the result. The study is one of its kinds to analyze the static and pair wise relationship among seven BSE indices along with the direction of their expected future movement that would help practitioners, policy makers and investors in anticipating the future movement of the indices. The Dickey-Fuller and Johanson test administered to analyze unit root and co-integration among the series in long run, followed by Granger causality test to observe the route of the short term relationship among various indices. The tests reveal uni-directional and in some cases bi-directional causality in selected indices. Further, it has been observed that due to co-integration, prices of different indices can’t move far away from one another [1]. This stochastic study delves volatility pattern of some major indices of Bombay stock exchange with the help of econometric tools. It clearly delineates nexus of all the indices and provided an explanation to appreciate concrete conduct of one series into a mutual relationship. Hence, investors or analyst may predict the movements, interdependency and their relationship in a significant manner.

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“…As a result, UAE investors seek capital on outside their home country as their UK investment options increase. Improving the transparency of transactions through information technology will increase the efficiency of Dubai's financial markets [5]. Different methods are used in each margin trading policy stage to estimate the impact of policies on China's stock market volatility, including but not limited to VAR models, impulse response functions and ARCH regression models [6].…”

Section: Introductionmentioning

confidence: 99%

Based on the Perspective of Fuzzy Theory and GARCH-MIDAS Method to See How Economic Policy Uncertainty Drives the Volatility of Asian Emerging Stock Market

2021

IJMC

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At present, there are two main phenomena in the classical measurement econometric research method: on the one hand, the same frequency data are used in the research process; on the other hand, many studies use low-frequency stock market data as the data index of the research object, which makes Asian stock market data has the same frequency as macro exogenous explanatory variables. The purpose of this paper is to solve the problem that the traditional co-frequency model cannot obtain the causal relationship between the macroeconomic explanatory variables and the fluctuations of emerging Asian stock markets due to the limitation of data frequency. This paper based on the fuzzy rationality hypothesis, using fuzzy mathematics and fuzzy statistical tools to improves the traditional GARCH-MIDAS model. Through the improved GARCH-MIDAS method, studied the fluctuation of emerging Asian stock markets. The results show that through the ability prediction for improved models, analyzed and found that both single-factor and multi-factor models have strong predictive power. By comparison, we can find that the multi-factor mixing model can better describe long-term component of price volatility in emerging Asian stock markets than the single factor mixing model.

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An investigation of stock market volatility: evidence from Dubai financial market

Cited by 5 publications

References 26 publications

Volatility in frontier markets: a Multivariate GARCH analysis

Volatility in frontier markets: a Multivariate GARCH analysis

Stochastic Pattern of Major Indices of Bombay Stock Exchange

Based on the Perspective of Fuzzy Theory and GARCH-MIDAS Method to See How Economic Policy Uncertainty Drives the Volatility of Asian Emerging Stock Market

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