On the Risk-Return Tradeoff in the Stock Exchange of Thailand: New Evidence

Jiranyakul, Komain

doi:10.5539/ass.v7n7p115

Cited by 7 publications

(6 citation statements)

References 38 publications

(33 reference statements)

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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%

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%

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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“…In accordance with the strong stream of financial press (e.g. see French et al, 1987;Hansson & Hordahl, 1998;Jiranyakul, 2011;Lanne & Saikkonen, 2004; Mandimika &Chinzara, 2012; who applied GARCH-M model to determine the risk-return relationship), this study also applied the GARCH-M model to detect the pricing of risk in an emerging market. The following general equation represents this model (Mandimika & Chinzara, 2012):…”

Section: Garch-m Modelmentioning

confidence: 99%

“…For instance, Li et al (2005) concluded negative risk premium for six out of 12 markets, and so does by Mandimika and Chinzara (2012) while examining the South African stock market. However, on the contrary, the study of French et al (1987) and Yu and Hassan (2008) in the Middle East and North African region and Jiranyakul (2011) in Thailand accredited a positive risk-return trade-off declaring a positive risk premium. Black (1976), and later on Christie (1982), Cheung and Ng (1992) and Duffee (1995), who mainly focused on the developed market of the NYSE, are considered to be the pioneers of asymmetry and leverage effect.…”

Section: Introductionmentioning

confidence: 99%

“…However, (1) several studies in the financial press (e.g. see Balios, 2008;Campbell & Hentschel, 1991;Dimitriou & Simos, 2011;Fraser & Power, 1997;French, Schwert, & Stambaugh, 1987;Glosten, Jagannathan, & Runkle, 1993;Hansson & Hordahl, 1998;Jiranyakul, 2011;Koutmos, Negakis, & Theodossiou, 1993;Lanne & Saikkonen, 2004;Lebaron, 1989;Lettau & Ludvigson, 2010;Li, Yang, & Hsiao, 2005;and Mandimika & Chinzara, 2012), ascribed mixed evidence regarding the existence of risk premium in the stock markets of Australia, the USA, Europe, Asia and Africa by applying the GARCH-M model. For instance, Li et al (2005) concluded negative risk premium for six out of 12 markets, and so does by Mandimika and Chinzara (2012) while examining the South African stock market.…”

Section: Introductionmentioning

confidence: 99%

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Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

Khan

Rehman

Khan

et al. 2016

Economic Research-Ekonomska Istraživanja

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This study analyses risk-return trade-off and behaviour of various volatility dynamics including: volatility, its persistence, mean reversion and speed of mean reversion along with asymmetry and leverage effect on the Pakistani stock market by employing aggregate (aggregate market level) and disaggregate (sectoral level) monthly data for the period from 1998 to 2012. Three generalised autoregressive conditional heteroscedasticity models were applied: GARCH (1,1) for various volatility dynamics; EGARCH for asymmetric and leverage effect and GARCH-M for pricing of risk. The outcomes of the study are as follows: first, the volatility shocks are quite persistent but with varying degrees across the sectors. Both the ARCH effect (short-term effect) and GARCH effect (long-term effect) play their role in generating conditional future stock returns volatility. Further, overall the volatility process is mean reverting; however, the speed of mean reversion varies across the sectors. Secondly, the current study finds little evidence of asymmetry and leverage effect at both aggregate and disaggregates data. Thirdly, the pricing of risk (positive risk premium) is also evident, particularly at the disaggregate data in the Pakistani stock market. Finally, this research study sets the implications for both the policy makers and investors.

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“…By analysing the risk-return relationship over time using rolling regressions, strong positive relationships between risk and expected return was found to persist throughout the sample period. Jiranyakul [15] investigated the link between risk-return trade-off in the Thai stock market using AR-GARCH-in-mean model on monthly data from January 1981 to December 2009. The author incorporated dummy variables in the conditional variance equations to capture the impact of the 1987 global stock market crash and the Asian 1997 financial crisis.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Stock Returns Volatility and the Risk-Return Nexus in the Nigerian Stock Market in the Presence of Shift Dummies

Kuhe

Chiawa

Nwaosu

et al. 2019

ARJOM

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Volatility and the trade-off between risk and return in stock markets is an important subject in financial theory which play significant role in investment decision making, portfolio selection, options pricing, financial stability, hedging and pair trading strategy among others. This study estimates stock return volatility and analyses the risk-return trade-off in the Nigerian stock market using symmetric GARCH (1,1)-in-mean, asymmetric CGARCH (1,1)-in-mean and EGARCH (1,1)-in-mean models with Generalized Error Distribution and Student-t innovation. Data on daily closing all share prices of the Nigerian stock exchange for the period 2nd January, 1998 to 9th January, 2018 are utilised. The data is further divided into three sub-periods of pre-crisis, global financial crisis and post crisis periods to allow volatility behaviour and the risk-return trade-off to be investigated across the sub-periods. Results showed evidence of volatility clustering, leptokurtosis, high persistence of shocks to volatility and asymmetry without leverage effects across the study periods. The persistence of shocks to volatility increased during the global financial crisis period with delayed reactions of volatility to market changes. However, by incorporating the exogenous breaks into the volatility models for the full study period, the shock persistence drastically reduced with faster reactions of volatility to market changes. The results of this study also found supportive evidence for a significant positive risk-return relationship in Nigerian stock market across various study sub-periods and model specifications meaning that investors in Nigerian stock market should be compensated for holding risky assets. The empirical findings of this study further suggest that the recent global financial crisis have not altered the market dynamics to distort the risk-return trade-off in Nigerian stock market indicating that expected returns are not driven by changes in the stock market volatility. The study provides some policy recommendations for investors and policy makers in the Nigerian stock market.

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On the Risk-Return Tradeoff in the Stock Exchange of Thailand: New Evidence

Cited by 7 publications

References 38 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

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

Estimating Stock Returns Volatility and the Risk-Return Nexus in the Nigerian Stock Market in the Presence of Shift Dummies

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