Effect of Volatility Changes on Emerging Stock Markets: The Case of Jordan

Orabi, Marwan Mohammad Abu; Alqurran, Talal

doi:10.5296/jmr.v7i4.7463

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…The estimated risk premium coefficients ( ) in the GARCH (2,1)-M models are also positive for both asset and volume of trade returns indicating that the conditional variances used as proxies for risk of returns are positively related to the levels of returns. This result corroborates the empirical findings of several authors [42,43,25,44,45] but contrary to the findings of several authors [46,47,48,49]. Tables 8 and 9, we observe also that by incorporating the structural break points in the volatility models, there are significant decreases in the values of shock persistence parameters ( ) in all the estimated asymmetric GARCH-type models.…”

Section: Parameter Estimates Of Symmetric and Asymmetric Volatility Msupporting

confidence: 89%

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Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

Kuhe

Chiawa

Nwaosu

et al. 2019

AJPAS

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This study investigated the impact of volatility shock persistence on the conditional variance in the Nigerian stock returns using symmetric and asymmetric higher order GARCH family models in the presence of random level shifts and non-Gaussian errors. The study utilised Bai and Perron methodology to detect structural breakpoints in the conditional variance of daily stock and volume of trade returns in the Nigerian stock market from 2nd January, 1998 to 22nd March, 2017. The study employed symmetric GARCH (3,2) and GARCH (2,1)-M models to estimate volatility of asset returns, symmetric GARCH (2,2) and GARCH (2,1)-M to model volatility of volume of trade returns and asymmetric EGARCH (2,2), TGARCH (3,2) and PGARCH (2,3) models to measure the volatility of asset returns as well as asymmetric EGARCH (2,1), TGARCH (1,3) and PGARCH (3,2) models to estimate volatility of volume of trade returns. These models were optimally selected using information criteria and log likelihood as the best fitting symmetric and asymmetric GARCH models to estimate the conditional volatility of asset and volume of trade returns in the Nigerian stock market with and without structural breaks. Results revealed that when random level shifts were ignored in volatility models, the shocks persistence were very high with long memory and variance explosion. But when the random level shifts were incorporated into the GARCH models, there was a significant reduction in the volatility shocks persistence and long memory. Moreover, volatility half-lives also declined drastically while accounting for these sudden level shifts in variance. The study found asymmetry without leverage effects as well as a positive risk-return tradeoff for both asset and volume of trade returns in the Nigerian stock market. The Nigeria banking reform of 2004, the Global Financial and Economic Crises, as well as other local events in Nigeria, were found to have negative and significant impacts on the Nigerian stock market. The study provided some policy recommendations.

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Section: Parameter Estimates Of Symmetric and Asymmetric Volatility Msupporting

confidence: 89%

“…Kuhe [24] found similar results. On modelling volatility in Nigerian stock market using GARCH family models with breaks see also the recent works of [25,26,27,28,29] among others for similar contributions.…”

Section: Literature Reviewmentioning

confidence: 93%

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

Kuhe

Chiawa

Nwaosu

et al. 2019

AJPAS

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“…Volatility is a statistical measure of the dispersion of returns for a given security or market index. Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index [1]. Commonly, the higher the volatility, the riskier the security.…”

Section: Fig-1: Daily Closing Rates Of Sample Stock Index Source: Webmentioning

confidence: 99%

Volatility Spillover Effect from Foreign Stock Exchanges to National Stock Exchanges

Shrimal¹

2016

SJEBM

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The present study examines the volatility spillover from foreign stock exchanges viz. NYSE Composite, NASDAQ 100, Nikkei 225 and FTSE 100 to national stock exchange viz. CNX Nifty. The time series daily closing data of seven years has been collected for the period starting from 1 st April 2008 to 31 st March 2015 for the study. For analysis econometric tools such as, descriptive analysis, ADF test for unit root, D-W Statistic, L-Jung Box Q Statistic, Breusch-Pagan Test, AR (1) Model, GARCH (1,1) and EGARCH (1,1) Model has been used. The objective of the paper is to determine if volatility spillover in one market influence the volatility of returns in the other market. GARCH (1, 1) and EGARCH (1,1) models used for modelling of spillover between returns of foreign stock exchanges and National stock exchange. This study concludes that volatility spillover between foreign stock exchanges and national stock exchange must be measured or predicted by EGARCH (1,1) model because it is more significant than GARCH (1,1) model.

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“…In line with the aim of the study, economic uncertainty may include exchange rate uncertainty, monetary policy uncertainty, inflation uncertainty and output uncertainty (Gan, 2014). Orabiand Alqurran (2015) argue that the broader definition of economic uncertainty can encompass variability, volatility and fluctuation or risk. With respect to the exchange rate uncertainty, stock market performance could be negatively depressed by the exchange rate volatility (Lawal and Ijirshar, 2015).…”

Section: Introductionmentioning

confidence: 96%

A Conceptual Model of Stock Market Efficiency: Does Economic Uncertainty Matter?

Yeap

Gan

2017

JCIT

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Many empirical works study the efficient market hypothesis by examining the relationship between the macroeconomic factors and the stock markets, however, there are scant studies focused on the economic uncertainty in a precise method by studying the stock market efficiency. The purpose of this paper is to propose the conceptual framework of stock market efficiency in economic uncertainty. The economic uncertainty, can be categorized into exchange rate uncertainty, monetary policy uncertainty (namely, interest rate uncertainty, money supply uncertainty), inflation uncertainty, and output uncertainty, and is associated with the stock market efficiency. The expected findings suggest that economic uncertainty contains useful information and is important in determining the stock market efficiency and could promote a better efficiency in stock market.

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Effect of Volatility Changes on Emerging Stock Markets: The Case of Jordan

Cited by 4 publications

References 17 publications

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

Volatility Spillover Effect from Foreign Stock Exchanges to National Stock Exchanges

A Conceptual Model of Stock Market Efficiency: Does Economic Uncertainty Matter?

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