Risk-return-volume relationship in an emerging stock market

Salman, Ferhan

doi:10.1080/13504850110105727

Cited by 20 publications

(13 citation statements)

References 11 publications

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“…To determine the volatility spillovers between the foreign exchange and equity markets, it is important first to determine the appropriate autoregressive model in order to ascertain the structure of the univariate model that characterises each of the series. A number of empirical studies support the use of an AR(1)‐GARCH(1,1) process for modelling stock returns and testing the weak‐form efficient market hypothesis in emerging stock markets (Zalewska‐Mitura and Hall, ; Salman, ; Shin, ; Abdmoulah, ). The rationale for using AR(1) representation in modelling the mean process of equity returns and the change in exchange rate is based on weak‐form efficiency and the random walk characteristics of the two variables (Fama, ).…”

Section: Data Analysis and Empirical Resultsmentioning

confidence: 99%

Volatility Spillovers between the Equity Market and Foreign Exchange Market in South Africa in the 1995‐2010 Period

Bonga‐Bonga

Hoveni

2013

South African J Economics

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This paper attempts to assess the extent of volatility spillovers between the equity market and the foreign exchange market in South Africa. Multistep family of the General Autoregressive Conditional Heteroskedasticity models are used for this end, whereby volatility shocks obtained from the mean equation estimation in each market are included in the conditional volatility of the other market, respectively. The paper selects the appropriate volatility models for each market following criteria such as covariance stationarity, persistence in variance and leverage effects. The finding of the paper indicates that there is a unidirectional relationship in terms of volatility spillovers from the equity market to the foreign exchange market. The paper supports the view that the extent of foreign participation in the South African equity market possibly contributes to this phenomenon.

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Section: Data Analysis and Empirical Resultsmentioning

confidence: 99%

Volatility Spillovers between the Equity Market and Foreign Exchange Market in South Africa in the 1995‐2010 Period

Bonga‐Bonga

Hoveni

2013

South African J Economics

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“…Many studies prove an empirical result of there is a significant positively linkage between realised return and systematic risk as measured by beta, and there is linear relationship arise between risk and return (44)(45)(46). (47) and (48) also proved that there was a linear relationship between beta and return in Istanbul stock exchange. Expected return-beta relationship is linear also was being proven in the Amman Stock Exchange (49)In Australian Industrial equity market, cross sectional relationship between beta and average return is also in a situation of linear(50).…”

Section: Systematic Risk/ Beta (Independent Variable)mentioning

confidence: 95%

“…Expected return is the dependent variable in this research. From the above literature review, risk and return always have a significant positive relationship between each other (37,38). Market risk is the most critical factors in affecting the return among other various systematic risks that will affect the stock return.…”

Section: Expected Return (Dependent Variable)mentioning

confidence: 99%

An Analysis of the Relationship between Risk and Expected Return in Malaysia Stock Market: Test of the CAPM

Rui¹,

Rasiah²,

Yen³

et al. 2018

IJET

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Investment theory describes the concept of relationship between risk and return. Capital Model Asset Pricing Model (CAPM) was based on the risk and return relationship. CAPM described that asset’s expected return that is above the risk free rate is directly related to the non-diversifiable risk that is measure by beta. Focus of this study is to identify the impacts of risk toward the stock return in Malaysia stock market during the year 2007 to 2015 by testing on the applicability of Capital Asset Pricing Model. The data is from monthly stock returns from 24 companies listed on the stock exchange for investigation. The analysis of monthly stock market closing indexes from using regression model was carried out on the standard CAPM model. When testing the CAPM model for the whole period, it has not showed strong evidence that support the validity of this model and in order to get better estimates, this study divided the whole sample into 3 sub periods of five years each. The study found high beta value does not related to higher level in stock return. The positive relationship between systematic risk and return does not have a strong evidence to support it. The research also identify that the securities market line has direct relationship between risk and return. The unsystematic risk does not have an effect on the return. It means that stock prices cannot be effectively predicted by CAPM and Malaysia Stock and the validity of CAPM does not exist in Malaysia Stock Exchange Market for the period 2007-2015 due to some limitations such as time frame, sample size and others. This paper suggest a different assets pricing model and takes into consideration of some related variables in predicting future stocks returns. This research provides important implication to investors, analysts, stock brokers, speculators, fund managers, practitioners, relevant authorities, and government.

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“…Secondly, this model underlines the importance of the autoregressive specification as well the conditional volatility in forecasting the mean of stock returns. A number of studies have supported the robustness of the AR(1)‐GARCH(1,1) for modelling stock returns in emerging market economies (Salman, 2002; Shin, 2005). Moreover, The results of the Schwarz Information Criteria (SIC) presented in Table A1 in the Appendix show that the mean process of market stock returns in south Africa is better represented by an AR(1) process.…”

Section: Methodsmentioning

confidence: 99%

The Predictability of Stock Market Returns in South Africa: Parametric vs. Non‐parametric Methods

Bonga‐Bonga

Mwamba

2011

South African J Economics

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This paper compares the forecasting performance of a sub‐class of univariate parametric and non‐parametric models in predicting stock market returns in South Africa. To account for conditional heteroskedasticity in stock returns data, the non‐parametric model is generated by the conditional heteroskedastic non‐linear autoregressive (NAR) model, while the parametric model is produced by the generalised autoregressive conditional heteroskedastic in mean (GARCH‐M) model. The results of the paper show that the NAR as a non‐parametric model performs better than the GARCH‐M model in short‐term forecasting horizon, and this indicates the importance of a distribution‐free model in predicting stock returns in South Africa.

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Risk-return-volume relationship in an emerging stock market

Cited by 20 publications

References 11 publications

Volatility Spillovers between the Equity Market and Foreign Exchange Market in South Africa in the 1995‐2010 Period

Volatility Spillovers between the Equity Market and Foreign Exchange Market in South Africa in the 1995‐2010 Period

An Analysis of the Relationship between Risk and Expected Return in Malaysia Stock Market: Test of the CAPM

The Predictability of Stock Market Returns in South Africa: Parametric vs. Non‐parametric Methods

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