scholarly journals Quantile Risk–Return Trade-Off

2021 ◽  
Vol 14 (6) ◽  
pp. 249
Author(s):  
Nektarios Aslanidis ◽  
Charlotte Christiansen ◽  
Christos S. Savva
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Quantile Regressions ◽  
Lower Tail ◽  
Trade Off ◽  
Risk Return ◽  
Market Portfolio ◽  
Asset Pricing Models ◽  
The Us ◽  
European Stock Markets

We investigate the risk–return trade-off on the US and European stock markets. We investigate the non-linear risk–return trade-off with a special eye to the tails of the stock returns using quantile regressions. We first consider the US stock market portfolio. We find that the risk–return trade-off is significantly positive at the upper tail (0.9 quantile), where the upper tail is large positive excess returns. The positive trade-off is as expected from asset pricing models. For the lower tail (0.1 quantile), that is for large negative stock returns, the trade-off is significantly negative. Additionally, for the median (0.5 quantile), the risk–return trade-off is insignificant. These results are recovered for the US industry portfolios and for Eurozone stock market portfolios.

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

2019 ◽  
pp. 1-15
Author(s):  
David Adugh Kuhe ◽  
Moses Abanyam Chiawa ◽  
Sylvester Chigozie Nwaosu ◽  
Jonathan Atsua Ikughur
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Stock Returns ◽  
Financial Stability ◽  
Global Financial Crisis ◽  
Stock Exchange ◽  
Stock Market Volatility ◽  
Trade Off ◽  
Risk Return ◽  
Market Changes

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.

Absence of the weekend effect and industry-style portfolios

2019 ◽  
Vol 12 (4) ◽  
pp. 463-475
Author(s):  
Selma Izadi ◽  
Abdullah Noman
Keyword(s):  
New York ◽  
Stock Market ◽  
Structural Breaks ◽  
Stock Exchange ◽  
Structural Break ◽  
Weekend Effect ◽  
Content Type ◽  
Market Portfolio ◽  
The Us ◽  
Using Data

Purpose The existence of the weekend effect has been reported from the 1950s to 1970s in the US stock markets. Recently, Robins and Smith (2016, Critical Finance Review, 5: 417-424) have argued that the weekend effect has disappeared after 1975. Using data on the market portfolio, they document existence of structural break before 1975 and absence of any weekend effects after that date. The purpose of this study is to contribute some new empirical evidences on the weekend effect for the industry-style portfolios in the US stock market using data over 90 years. Design/methodology/approach The authors re-examine persistence or reversal of the weekend effect in the industry portfolios consisting of The New York Stock Exchange (NYSE), The American Stock Exchange (AMEX) and The National Association of Securities Dealers Automated Quotations exchange (NASDAQ) stocks using daily returns from 1926 to 2017. Our results confirm varying dates for structural breaks across industrial portfolios. Findings As for the existence of weekend effects, the authors get mixed results for different portfolios. However, the overall findings provide broad support for the absence of weekend effects in most of the industrial portfolios as reported in Robins and Smith (2016). In addition, structural breaks for other weekdays and days of the week effects for other days have also been documented in the paper. Originality/value As far as the authors are aware, this paper is the first research that analyzes weekend effect for the industry-style portfolios in the US stock market using data over 90 years.

Weather, investor psychology and stock returns: evidence from Fiji's stock market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janesh Sami
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Sunshine Duration ◽  
Market Returns ◽  
Modeling Framework ◽  
Stock Market Returns ◽  
Content Type ◽  
Asset Pricing Models ◽  
Weather Changes ◽  
Practical Implications

PurposeThis paper investigates whether weather affects stock market returns in Fiji's stock market.Design/methodology/approachThe author employed an exponential general autoregressive conditional heteroskedastic (EGARCH) modeling framework to examine the effect of weather changes on stock market returns over the sample period 9/02/2000–31/12/2020.FindingsThe results show that weather (temperature, rain, humidity and sunshine duration) have robust but heterogenous effects on stock market returns in Fiji.Research limitations/implicationsIt is useful for scholars to modify asset pricing models to include weather-related variables (temperature, rain, humidity and sunshine duration) to better understand Fiji's stock market dynamics (even though they are often viewed as economically neutral variables).Practical implicationsInvestors and traders should consider their mood while making stock market decisions to lessen mood-induced errors.Originality/valueThis is the first attempt to examine the effect of weather (temperature, rain, humidity and sunshine duration) on stock market returns in Fiji's stock market.

Stock Market Anomalies

2017 ◽  
Author(s):  
Steve Fan ◽  
Linda Yu
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Financial Economic ◽  
Economic Research ◽  
Market Anomalies ◽  
Cross Sectional ◽  
Additional Risk ◽  
Asset Pricing Models ◽  
Stock Market Anomalies

Stock market anomalies representing the predictability of cross-sectional stock returns are one of most controversial topics in financial economic research. This chapter reviews several well-documented and pervasive anomalies in the literature, including investment-related anomalies, value anomalies, momentum and long-term reversal, size, and accruals. Although anomalies are widely accepted, much disagreement exists on the underlying reasons for their predictability. This chapter surveys two competing theories that attempt to explain the presence of stock market anomalies: rational and behavioral. The rational explanation focuses on the improvement of the existing asset pricing models and/or searching for additional risk factors to explain the existence of anomalies. By contrast, the behavioral explanation attributes the predictability to human behavioral biases in collecting and processing financial information, as well as in making investment decisions.

The Risk-Return Trade-Off in a Liberalized Emerging Stock Market: Evidence from Vietnam

2015 ◽  
Vol 53 (4) ◽  
pp. 746-763 ◽  
Author(s):  
Kuangnan Fang ◽  
Ji Wu ◽  
Cuong Nguyen
Keyword(s):  
Stock Market ◽  
Trade Off ◽  
Risk Return

Risk-Reward Trade-Off and Volatility Performance of Islamic Versus Conventional Stock Indices: Global Evidence

2020 ◽  
Vol 23 (01) ◽  
pp. 2050002 ◽  
Author(s):  
Ahmad Abu-Alkheil ◽  
Walayet A. Khan ◽  
Bhavik Parikh
Keyword(s):  
Stock Returns ◽  
Trade Off ◽  
Risk Return ◽  
Stock Indices ◽  
The Pacific ◽  
High Return ◽  
The Impact ◽  
Primary Support ◽  
Sortino Ratio ◽  
Global Evidence

In this paper, we compare the performance of Islamic stock indices (ISI) and conventional stock indices (CSI) from FTSE, DJ, MSCI, S&Ps and Jakarta series using common risk-return metrics. The sample consists of 64 ISI and CSI, and covers the period from 2002 to 2017. The majority of the stock indices are from the Pacific Rim countries’ stock markets. Additionally, we employ the GARCH-M model to examine the impact of past volatility on spot returns. Findings suggest that the ISI are less sensitive to the average market movements compared to the CSI, but surprisingly offer similar raw returns suggesting primary support for the low risk-high return paradox. On further examination, results reveal that M2, Omega, Sharpe and Treynor measures indicate that ISI underperform CSI while Jensen’s alpha and Sortino ratio put ISI ahead of CSI. Moreover, findings show that pre-crisis winners (CSI) were losers during the 2008 crisis but subsequently recovered and ended up with higher returns than ISI. Findings also show that the previous volatility of stock returns can be potentially used for predicting future returns.

Risk-Return Tradeoff in U.S. Stock Returns over the Business Cycle

2011 ◽  
Vol 47 (1) ◽  
pp. 137-158 ◽  
Author(s):  
Henri Nyberg
Keyword(s):  
Stock Market ◽  
Business Cycle ◽  
Stock Returns ◽  
Regime Switching ◽  
Probit Model ◽  
Expected Return ◽  
Stock Market Returns ◽  
Risk Return ◽  
The Business Cycle ◽  
Cycle Indicator

AbstractIn the empirical finance literature, findings on the risk-return tradeoff in excess stock market returns are ambiguous. In this study, I develop a new qualitative response (QR)-generalized autoregressive conditional heteroskedasticity-in-mean (GARCH-M) model combining a probit model for a binary business cycle indicator and a regime-switching GARCH-M model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional intertemporal capital asset pricing model (ICAPM), there is a positive risk-return relationship between volatility and expected return independent of the state of the economy.

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