Is there an Intertemporal Risk-return Trade-off for Chinese Stock Markets

2013 ◽  
Vol 5 (5) ◽  
pp. 66-73 ◽  
Author(s):  
Ziran Li ◽  
Hongmei Chen ◽  
Ying Gao
Keyword(s):  
Stock Markets ◽  
Trade Off ◽  
Risk Return ◽  
Chinese Stock Markets

Is there a risk-return trade-off? Evidences from Chinese stock markets

2008 ◽  
Vol 3 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Dongmin Kong ◽  
Hening Liu ◽  
Le Wang
Keyword(s):  
Stock Markets ◽  
Trade Off ◽  
Risk Return ◽  
Chinese Stock Markets

scholarly journals Risk-return trade-off for European stock markets

2016 ◽  
Vol 46 ◽  
pp. 84-103 ◽  
Author(s):  
Nektarios Aslanidis ◽  
Charlotte Christiansen ◽  
Christos S. Savva
Keyword(s):  
Stock Markets ◽  
Trade Off ◽  
Risk Return ◽  
European Stock Markets

scholarly journals STOCK RETURNS, VOLATILITY AND MEAN REVERSION IN EMERGING AND DEVELOPED FINANCIAL MARKETS

2018 ◽  
Vol 24 (3) ◽  
pp. 1149-1177 ◽  
Author(s):  
Rizwan Raheem AHMED ◽  
Jolita VVEINHARDT ◽  
Dalia ŠTREIMIKIENĖ ◽  
Saghir Pervaiz GHAURI
Keyword(s):  
Emerging Markets ◽  
Stock Returns ◽  
Stock Markets ◽  
Mean Reversion ◽  
Conditional Variance ◽  
Trade Off ◽  
Risk Return ◽  
Developed Markets ◽  
The Mean ◽  
Developed Stock Markets

The objective of this research is to measure and examine volatilities between important emerging and developed stock markets and to ascertain a relationship between volatilities and stock returns. This research paper also analyses the Mean reversion phenomenon in emerging and developed stock markets. For this purpose, seven emerging markets and five developed markets were considered. Descriptive statistics showed that the emerging markets have higher returns with the higher risk-return trade-off. In contrast, developed markets have low annual returns with a low risk-return trade-off. Correlation analysis indicated the significant positive correlation among the developed markets, but emerging and developed markets have shown relatively insignificant correlation. Results of ARCH and GARCH revealed that the value of likelihood statistics ratio is large, that entails the GARCH (1,1) model is a lucrative depiction of daily return pattern, that effectively and efficiently capturing the orderly reliance of volatility. The findings of the study showed that the estimate ‘β’ coefficients given in conditional variance equation are significantly higher than the ‘α’, this state of affair entails that bigger market surprises tempt comparatively small revision in future volatility. Lastly, the diligence of the conditional variance estimated by α + β is significant and proximate to integrated GARCH (1,1) model, thus, this indicates, the existing evidence is also pertinent in order to forecast the future volatility. The results signified that the sum of GARCH (1,1) coefficients for all the equity returns’ is less than 1 that is an important condition for mean reversion, as the sum gets closer to 1, hence the Mean reversion process gets slower for all the emerging and developed stock markets.

THE EFFECTS OF GOLD, STOCK MARKETS AND GEOPOLITICAL UNCERTAINTY ON BITCOIN PRICES AND VOLATILITY

2020 ◽  
pp. 2050020
Author(s):  
NIKOLAOS A. KYRIAZIS
Keyword(s):  
Stock Markets ◽  
Risk Index ◽  
Conditional Heteroskedasticity ◽  
Trade Off ◽  
Economic Agents ◽  
Risk Return ◽  
Volatility Index ◽  
Negative Impacts ◽  
Uncertainty Indices ◽  
Best Fit

This paper investigates the nexus between Bitcoin, gold and highly innovative uncertainty indices by employing alternative Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized ARCH (GARCH) specifications. The period examined covers from March 2012 up to March 2020. Econometric outcomes indicate that Bitcoin returns and volatility are positively influenced by gold returns and the S&P500 volatility index (VIX). Nevertheless, it is revealed that the innovative Geopolitical Risk Index by Caldara and Iacoviello (2019) exerts negative impacts on Bitcoin markets. Evidence indicates that the Simple Asymmetric ARCH methodology provides the best fit for the purposes of estimations. Our findings cast light on the important aspects of Bitcoin behavior and provide guidance for decision-making about portfolios that contain Bitcoin and gold. This enables economic agents to ameliorate their risk–return trade-off depending on the fluctuations in stock markets and geopolitical uncertainty.

scholarly journals Risk-Return Trade-Off for European Stock Markets

2013 ◽  
Author(s):  
Nektarios Aslanidis ◽  
Charlotte Christiansen ◽  
Christos S. Savva
Keyword(s):  
Stock Markets ◽  
Trade Off ◽  
Risk Return ◽  
European Stock Markets

Risk-return tradeoff in Chinese stock markets: some recent evidence

2015 ◽  
Vol 10 (3) ◽  
pp. 448-473 ◽  
Author(s):  
Menggen Chen
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Stock Exchange ◽  
Structural Break ◽  
Significant Risk ◽  
Content Type ◽  
Risk Return ◽  
Emerging Stock Markets ◽  
Chinese Stock Markets ◽  
Over Time

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Originality/value – 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.

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