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The purpose of this paper is to pay more attention to four different research questions at least. One is that this study intends to explore the changes of the risk-return relationship over time, because the institutions and environment have changed a lot and might tend to influence the risk-return regime in the Chinese stock markets. The second question is whether there is any difference for the risk-return relationship between Shanghai and Shenzhen stock markets. The third question is to compare the similarities and dissimilarities of the risk-return tradeoff for different frequency data. The fourth question is to compare the explanation power of different GARCH-M type models which are all widely used in exploring the risk-return tradeoff.

This paper investigates the risk-return tradeoff in the Chinese emerging stock markets with a sample including daily, weekly and monthly market return series. A group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. Additionally, some diagnostic checks proposed by Engle and Ng (1993) are used in this paper, and this will help to assess the robustness of different models.

The empirical results show that the dynamic risk-return relationship is quite different between Shanghai and Shenzhen stock markets. A positive and statistically significant risk-return relationship is found for the daily returns in Shenzhen Stock Exchange, while the conditional mean of the stock returns is negatively related to the conditional variance in Shanghai Stock Exchange. The risk-return relationship usually becomes much weaker for the lower frequency returns in both markets. A further study with the sub-samples finds a positive and significant risk-return trade-off for both markets in the second stage after July 1, 1999.

This paper extends the existing related researches about the Chinese stock markets in several ways. First, this study uses a longer sample to investigate the relationship between stock returns and volatility. Second, this study estimates the returns and volatility relationship with different frequency sample data together. Third, a group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. In particular, the author employs the Component GARCH-M model which is relatively new in this line of research. Fourth, this study investigates if there is any structural break affecting the risk-return relationship in the Chinese stock markets over time.

This paper aims to examine the risk premium for investors in a changing information environment in the Taiwan, New York and London real estate markets from March 2006 to November 2014. This study attempts to quantify behavioral expectations regarding (or motivation for) investment in the Taiwanese real estate in a changing information environment.

This paper uses the rolling generalised autoregressive conditionally heteroskedastic in mean (GARCH-M) methodology which fixes the problem of conventional GARCH-M methodology.

Empirical evidence suggests that the time-varying risk premium changed for the Taiwan real estate market with a new information set. The risk premium changed from 1.305 per cent per month to −7.232 per cent per month. The study also found persistent volatility shocks from March 2006 to November 2014. No such evidence was found for the New York and London real estate markets. Overall, this study finds evidence of a time-varying risk premium, partly explainable by governmental policies and partly unexplainable.

The use of the index of Standard and Poor’s Taiwan Real Estate Investment Trusts to study the Taiwan real estate industry may have aggregation effects in result.

The present study will provide guidance to investors as well as policymakers regarding the Taiwan real estate market.

This study uses the rolling GARCH-M model, which is a first for the Taiwan real estate market.

The purpose of this paper is to empirically examine stock returns behavior during financial crises for an emerging market from 1992 to 2009. The authors identify episodes of significant price declines “crashes” and watch the stock price behavior during these episodes.

This paper examines seven historical episodes of stock market crashes and their aftermath in the ASE over the last 18 years. The authors examine the behavior of stock returns and volatility in ASE during global, regional and local events. For this purpose the GARCH‐M model is used to capture changes in variance. The data covers the period from January 1, 1992 to July 2, 2009 with different data frequency of daily, weekly and monthly closing prices for ASE general weighted price index. The authors use the crisis specification adopted by Mishkin and White where they define stock market crash as 20 percent decline in the stock market, and the one adopted by Patel and Sarker where they use a 35 percent or more fall in emerging stock market from its historical maximum as a definition of stock market crash, and the authors extend by adopting a third scenario to account only for the 2008‐2009 crisis.

The results show that crises in general have negative impact on stock returns for all sectors, with the banking sector being the most affected. The effect of the 2008‐2009 crash is the most severe, with the largest drop in stock prices and high volatilities. The paper provides an evidence of high persistence in volatility and strong reverse relationship between stock return and its volatility before and after the crises.

The paper does not include rest‐of‐the‐world economies.

Stock return behavior change around financial crises, it can help the investment world and the academics predict stock return behavior and the dynamics of the first two moments during crises.

The authors use three crisis specifications in one paper adopted by Mishkin and White (2002), Patel and Sarker (1998) and extend by adopting a third scenario to account only for the 2008‐2009 crisis. The paper tests for robustness of the results using daily, weekly, and monthly frequencies. Few studies have examined the behavior of stock returns and volatility during financial crises with the majority of work done on developed markets.

In any stock market, volatility is a significant factor in strengthening their asset pricing. The upsurge in volatility in the stock market can activate and bring changes in the financial risk. According to financial conventional theory, the stakeholders (investors) are selected to be balanced and variations in pertinent risk are also to be anticipated due to the outcome of the drive-in basic factors in Indian stock markets. The hypothesis shows that there are actions in systematic and unsystematic risks that are determined by volatility. It is allied to sentiment-driven in the trader movement.

The paper used the methodology of generalized autoregressive conditional heteroskedasticity-in mean GARCH-M and exponential GARCH-M (E-GARCH-M) methods on the Indian stock market. The data have been covered from 2000 to 2019.

Finally, the study suggests that due to the unfitness of the capital asset pricing model (CAPM), the selection has enhanced with sentiment is an important risk factor.

The investor sentiment and stock return volatility statement are established by using the investor sentiment amalgamated stock market index built.

The outcome of the study shows that there is an important association between stakeholder (investor) sentiment and stock return, in case of volatility behavioural finance can significantly explain the behaviour of stock returns on the Indian Stock Exchange.

The purpose of this paper is to examine the impact of the future of trading on volatility as well as the efficiency of the stock market of BRIC (Brazil, Russia, India and China) countries. This study also investigates the presence of day‐of‐the‐week effect in BRIC countries' stock market.

This study uses closing prices of IBrx‐50 for Brazil, RTSI for Russia, Nifty for India and CSI300 for China to represent the stock market of BRIC countries. The Run and ACF tests are used to see impact on market efficiency. GARCH M model is used to see the impact on volatility and day‐of‐the week effect.

The insignificant coefficient of variance in the conditional mean equation of GARCH M implies that the market doesn't provide higher returns during the high volatility period. The results of the Run test showed that the Russian stock market became efficient after introduction of future trading. However, ACF showed no effect of introduction of future trading on autoregressiveness of stock returns. The result of GARCH M indicates that future trading led to reduction in the volatility of the Indian stock market. There are some evidences of presence of day‐of‐the‐week effect in the Indian stock market.

This paper will help regulators to form appropriate policies as the market would have to pay a certain price, such as loss of market efficiency, for the sake of market stabilization. This will also help investors to make investment decisions, especially investing in these indices as the existence of the significant day‐of‐the‐week effect and the inefficiency in the stock market would be very useful for developing investment strategies.

This paper will be useful for both investors and regulators in decision making.

To investigate the performance of moving average trading rules in an emerging market context, namely that of the Jordanian stock market.

The conditional returns on buy or sell signals from actual data are examined for a range of trading rules. These are compared with conditional returns from simulated series generated by a range of models (random walk with a drift, AR (1), and GARCH‐(M)) and the consistency of the general index series with these processes is examined. Sensitivity analysis of the impact of transaction costs is conducted and standard statistical testing is extended through the use of bootstrap techniques.

The empirical results show that technical trading rules can help to predict market movements, and that there is some evidence that (short) rules may be profitable after allowing for transactions costs, although there are some caveats on this.

New results for the Jordanian market; use of sensitivity analysis to investigate robustness to variations in transactions costs.

The purpose of this paper is to test prominent calendar anomalies for Indian securities markets those are commonly reported for advanced markets.

The study considers closing values of 11 different indices of National Stock Exchange India, for the period 1994-2014. By using dummy variable regression technique, five different calendar anomalies namely day of the week effect, month of the year effect, mid-year effect, Halloween effect, and trading-month effect are tested. Also, the evidence of volatility clustering has been tested through the application of generalized autoregressive conditional heteroscedasticity (GARCH)-M models.

The results display weak evidence in support of a positive Wednesday effect. The results also display weak evidence in support of a positive April and December effect. The results show strong evidence in support of a positive September effect. The Halloween effect was not found significant. The test of mid-year effect provides evidence that the returns obtained on the second-half or the year are considerably higher than those obtained during the first half. The test of interactions effects showed possible presence of interactions among various effects. The GARCH-based tests display strong evidence in support of volatility clustering.

The results have several implications for investors, regulators, and researchers. For investors, the trading strategies based on results obtained have been discussed. Similarly, certain key implications for regulators have been described.

The originality of the paper lies in the long time frame and multiple indices covered. Also, the study analyses five different calendar anomalies and the interactions among these effects. These analyses provide useful insights regarding returns predictability for the Indian securities markets.

Using the economic policy uncertainty (EPU) index as a proxy for the level of EPU, we study the impact of the level of EPU on the conditional mean of market returns and we examine the predictive power of EPU on future market returns.

We employ a GARCH-in-Mean model with exogenous variables.

The results show that even after controlling for business cycle effects, EPU is inversely related to contemporaneous market returns. Particularly, the authors find that the negative impact of EPU subsists only during recessions or recessionary states of the economy, and has no discernible effects during expansionary periods.

This is the first study to examine the predictive power of EPU on future market returns.

The purpose of this paper is to examine volatility and the weak-form efficient market hypothesis (random walk) of world spot crude oil market.

The study uses the generalized autoregressive conditional heteroskedasticity (GARCH-M), exponential generalized autoregressive conditional heteroskedasticity (EGARCH), and threshold GARCH (TGARCH) models. The data are selected from three markets: Dubai Vetch (DV), West Texas Intermediate, and Europe Brent Spot Price.

The weak-form efficient market (random walk) hypothesis was rejected for all estimated GARCH-M, EGARCH, and TGARCH models, indicating that these markets are inefficient and predictable. For daily data, the empirical results showed the presence of asymmetric effects, and the conditional variance process was found to be highly persistent.

This study is unique in its nature as it examines three markets on three continents. In addition, one of these markets (DV) was not carried out by the previous study. This work takes into account the market location.

Finance theory such as Merton’s ICAPM suggests that there should be a positive relationship between the expected return and risk. Empirical evidence on this relationship, however, is far from conclusive. Building on the recent econometric research on this topic such as Lundblad (2007) and Hedegaard and Hodrick (2016), we estimate the risk-return relation implied in the ICAPM using a long sample (1962~2016) of daily, weekly, and monthly excess stock returns in Korea. More specifically, we estimate various volatility models including GARCH-M using the overlapping data inference (ODIN) method suggested by Hedegaard and Hodrick (2016), as well as the traditional maximum likelihood estimation methodology. For the full sample period, we fail to find a positive risk-return relationship that is significant and robust. For the subsample period from 1998 to 2016, however, we find a significantly positive risk-return relation for GARCH-M model regardless of return intervals and estimation methods. This result is also robust to using other specifications such as EGARCH-M which includes the leverage effect of the variance process and EGARCH-M-GED whose conditional distribution has fatter tails. Our findings suggest that there is indeed a positive relationship between the expected return and risk in the Korean stock market, at least for the period after 1998.