Positive Risk-return Trade-off Research Articles

State-dependent intertemporal risk-return tradeoff: Further evidence

We suggest that the intertemporal risk-return tradeoff is not necessarily positive but rather state dependent. We further explore the state dependent risk-return relation by examining how the positive risk-return relation is distorted in response to various market conditions, including extreme price changes, differing levels of investor sentiment, the introduction of stock options, and throughout business cycles. The tendency for uninformed investors to be optimistic (pessimistic) in response to good (bad) market news cause overpricing (underpricing), and the resulting trade activity of arbitrageurs that distorts the positive risk-return tradeoff, is documented consistently across these environments. We find that the attenuation (reinforcement) of the positive risk-return relation under investors’ optimistic (pessimistic) expectations is stronger in high (low) sentiment periods, in the presence of extreme returns, in the period after stock options became available, and during expansionary periods. We argue that the asymmetric intertemporal risk-return relation is a consequence of rational arbitrageurs’ trading to exploit mispricing through the selling of overpriced stocks conditional on good news and buying underpriced stocks conditional on bad news.

Does Risk Return Trade-off Hold in Base Metal and Bullion Commodity Assets? A Bayesian Change Point Analysis

The risk-return trade-off is fundamental to portfolio investing whose stability is critical for portfolio optimisation. Since the relationship is dynamic, the portfolio manager should know the point of change and, thereafter duration of the changed period with certainty. First, we have done Bayesian change point analysis, and then based on the analysis, the study identifies the regimes having equal statistical variance along with the corresponding average return in two most popular commodities, that is, copper and gold. It is found that risk-return trade-off is not stable. Further, in a higher volatility regime, only gold can be considered as a diversifiable commodity because a positive risk-return trade-off holds. But in a low volatility regime, both commodities lose their diversification properties as the risk-return relation becomes negative.

Fund-level Investor Sentiment and Mean-variance Relation: Evidence From Singapore-listed ETFs

This paper tests the influences of fund-level sentiment on the mean-variance relation in ETF market. We find that in low (high)-sentiment periods, the expected excess return is positively (negatively) related to the conditional variances. Sentiment traders undermine the otherwise positive risk-return tradeoff and even twist it into a negative one in high-sentiment periods. The impact of sentiment is stronger during the global pandemic.

Risk, ambiguity, and equity premium: International evidence

We empirically examine the relation between risk, ambiguity, and market equity premium in 21 international stock markets. After estimating each country's degree of ambiguity from intraday data of iShares country ETFs (exchange-traded funds), we find a positive risk-return tradeoff. Investors' attitudes toward ambiguity is conditional on their expectations of probability of gains, but its relationship is nonlinear. This nonlinearity in ambiguity premium is more prominent in emerging markets. Additionally, we conduct a cross-country analysis, which shows that cross-country variations in cultural and institutional factors affect investors' behavior. Our international evidence supports that ambiguity alongside risk are the key determinants of investors' decision-making.

RISK-RETURN TRADEOFFS IN INDIAN STOCK MARKET: EVIDENCE FROM GARCH MODEL APPROACH

The objective is to examine the risk-return tradeoff in the Indian stock market. The sample period of study is from January 4, 2000 to December 31, 2020. The empirical results shows existence of risk-return tradeoff in the BSE. A positive risk-return tradeoff is found for monthly & annual return series. The market has weak risk-return relationship in daily return series. The CGARCH (1,1) captures the asymmetric volatility effect for all the different frequency based returns. The study has implications for the investors. The riskreturn relationship is stronger and significant in longer duration of investment. The market gives higher return when there is a high risk.

Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration

Given that the United States is an engine of global stock market while China is the largest emerging market with a cornucopia of anomalies in particular, it is vital to investigate the risk-return relationship in the two markets. This paper brings new insights not only into risk-return tradeoff, but also to the leverage effect, with the application of the fractionally co-integrated vector auto-regression (FCVAR) model capturing the fractional cointegrated relationship and long memory property. Results show that China stock markets own the property of double long memory but the US markets don’t. Most of all, in the US market, a positive risk-return tradeoff exists for the whole sample while after the crisis, even we find the negative relation, it’s not a volatility feedback effect but low risk and high returns. However, there is only a volatility feedback effect in China stock markets. Besides, there is a leverage effect in the US market, while Chinese market exhibits a reverse one, another anomaly, indicating significant difference in the two markets again.

Value at risk and returns of cryptocurrencies before and after the crash: long-run relations and fractional cointegration

Cryptocurrency has become an increasingly important investment vehicle, thus the long-run relationship between risk and return of cryptocurrency is vital for both investors and policy-makers. We apply the Fractionally Cointegrated Vector Autoregression (FCVAR) model and investigate the risk-return relationship. This has not been studied previously, much less disintegrating the series into periods of pre-crash, post-crash, and the full sample. Empirical results indicate that risk series in all eight cryptocurrency markets exhibit long-memory property, and there is a long-run fractional cointegration relationship between the risk of altcoins and Bitcoin. Most importantly, though a positive risk-return tradeoff is found in the full sample, there are big differences between results of pre-crash and post-crash. The time horizon set to 14 days, all the eight currencies exhibit traditional risk-return tradeoff after the crisis, whereas the effect doesn’t exist before the crisis, with the exception of Dash and Doge. In the same vein, there is no leverage effect in all eight currencies before the crisis but this effect is present in three cryptocurrencies after the crisis. The differences indicate that investors are more cautious, increasing risk awareness and demanding higher compensation for risk after the crash. The above results are robust when time horizon is 30 days.

Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin

This study examines the link between returns and volatility of Bitcoin, at both contemporaneous and intertemporal levels, employing high-frequency data. The intraday price variability is proxied by four different measures, namely realized variance, jump variation, downside realized semivariance, and negative signed jump variation. The empirical analysis suggests that all realized volatility proxies have a significant and negative contemporaneous relation with Bitcoin returns. On the other hand, there is weak evidence of a negative intertemporal relation between returns and realized variance, jump variation, and downside realized semivariance. Accordingly, the existence of a positive risk-return trade-off in Bitcoin markets seems to be unsubstantiated. The findings are robust, even after controlling for a number of relevant determinants of the price formation process of Bitcoin.

Cross-Sectional Relationship Between Beta and Realized Returns in Emerging Markets

Abstract This paper examines the conditional risk-return relationship caused by the impact of using realized returns as a proxy for expected returns, which requires a separation of negative and positive market premiums. Following the methodology of Pettengill et al. (1995), we test the cross sectional relationship between beta and realized returns on the Amman Stock Exchange (ASE) for ten beta sorted portfolio over the period of January 1993 to December 2016. The empirical results suggest that the traditional two-pass approach produces an insignificant relationship between beta and realized returns in most of the sample period. However, when adjusting for negative market premiums, the results show a significant and consistent relationship for all the testing periods and samples. However, a guaranteed reward for holding extra risk occurred only in the period 2001 –2008, which suggests an assurance of positive risk-return tradeoff during bull markets. JEL Classifications: G11, G12, G15, C21 Asset Pricing, Emerging Markets, Conditional Relationship, Beta, Market Premium

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

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.

The VIX, the Variance Premium, and Expected Returns*

AbstractExisting studies find conflicting estimates of the risk–return relation. We show that the trade-off parameter is inconsistently estimated when observed or estimated conditional variances measure risk. The inconsistency arises from misspecified, unbalanced, and endogenous return regressions. These problems are eliminated if risk is captured by the variance premium (VP) instead; it is unobservable, however. We propose a 2SLS estimator that produces consistent estimates without observing the VP. Using this method, we find a positive risk–return trade-off and long-run return predictability. Our approach outperforms commonly used risk–return estimation methods, and reveals a significant link between the VP and economic uncertainty.

Gambler's attention and the mean-variance relation: Evidence from China

Research from psychology suggests that gambler's fallacy and limited attention matter for individual decision making involving risk. We dub this combination “gambler's attention” and use it to provide a behavioral perspective on the debate over the market's mean-variance relation. A gambler's attention index is developed to divide the sample period into high-attention and low-attention regimes. Using data from China, we find clear-cut evidence that the market's mean-variance relation is significantly positive in low-attention periods but not in high-attention periods. The results are consistent with the notion that gambler's attention undermines an otherwise positive risk-return tradeoff in high-attention periods.

The Asymmetric Conditional Beta-Return Relations of REITs

The traditional modern portfolio model posits that return is a function of beta—the stock’s sensitivity to market movements. However, much research suggests that on an empirical basis this expectation does not hold. Pettengill, Sundaram and Mathur (1995) [PSM] suggest that the problem is that expected risk-return relationships are positive, but actual outcomes can vary. Thus we implement the PSM procedure to determine if we can obtain better explanatory ability for REIT returns relative to beta. Using PSM’s suggested technique, we find evidence that REIT stocks with higher betas have more positive returns when the realized market returns exceed risk-free rates and more negative returns when the realized market returns fall below risk-free rates. Furthermore, we form REIT portfolios based on betas and find that similar results hold for portfolio level: REIT portfolios with higher betas have more positive returns when the realized market returns exceed risk-free rates and more negative returns when the realized market returns fall below risk-free rates. We also examine beta relative to up and downside risk as suggested in Harlow and Rao (1989) [HR]. By using HR conditional beta approach, we find that REIT investors seem to view losses differently than gains. Our study is to determine if we can validate the positive risk-return trade-off predicted by CAPM and to show that a significant and positive systematic beta-return relation exists in both static and conditional CAPM model settings. We believe that our research effort is the first to incorporate both static beta estimation and asymmetric beta estimation to show a significantly positive risk-return trade-off in the industry of real estate investment trusts. Using HR generalized Mean-Lower Partial Moment Asset Pricing Model, we confirm that REIT stocks with higher downside betas have higher average returns, but upside betas are insignificant in the beta-return relation for REITs. We find similar results for portfolio level as well. Consistent with the findings on real estate market index returns in Cheng (2005), our results go beyond his findings to highlight the significance of downside beta risk in REIT industry during the recent global financial crisis.

Diversification Effects on the Banks’ Return and Risk

This article analyses the diversification effects on the exchange listed EU banks’ risk-return framework. Second banking directive of 1989 has initiated the boom of activity diversification in Europe. The empirical results indicated to the statistically significant diversification premium on the banks’ performance, using the non-interest income share, together with its quadratic term, as indicator of banking diversification. The same holds for the banks with the diversified assets. In terms of revenue diversification, diversified banks are less profitable. Due to the statistically insignificant outcomes, in case of total and bank-specific risk analysis, it is difficult to make an implicit conclusion, how the diversification decision affects those types of risk. But, in terms of systematic risk there is strong statistical evidence that more diversified banks are exposed to higher levels of market risk. As a result, for the sample of European banks, the positive risk-return trade-off was not found.

A new approach to risk-return trade-off dynamics via decomposition

This paper revisits the puzzling time series relation between risk premium and conditional volatility by proposing a flexible risk-return trade-off that allows for a variety of possible shapes and incorporates potential nonlinearities inherent in excess return dynamics. We derive this flexible risk-return relation using the decomposition approach of Anatolyev and Gospodinov (2010), which splits excess returns into the product of absolute returns and signs. Using this decomposition strategy, we study four major international financial markets. The empirical results support a significant and positive risk-return trade-off that is driven by conditional volatility, market timing and the interdependence between the two components, which is generically related to return skewness.

Is there a Positive Risk-Return Tradeoff? A Forward-Looking Approach to Measuring the Equity Premium

This article revisits the puzzling time-series relation between risk and return on the stock market portfolio. It replaces the standard ex post mean returns with forward-looking calculations of the equity risk premium derived from the classic Gordon stock valuation model. The article estimates the equity premium for several industrialised markets and finds that conditional market risk is significantly priced in the context of asset pricing theory both in the short- and long-run using various specifications for volatility. Findings herein lend credible support for the presence of a positive intertemporal risk-return relation and suggest that perhaps ex post realised returns are unjustifiably used to make ex ante inferences regarding expected returns and to motivate asset pricing tests.

Predicting Stock Returns in the Presence of Uncertain Structural Changes and Sample Noise

Expected returns are believed to be persistent and mean-reverting, and therefore often modeled as an AR(1) process. We propose to use instead a CIR model, which preserves the former behavior but implies additional important economically motivated characteristics such as a positive risk-return trade-off and a time-varying volatility. A CIR-based predictive system without predictors also reproduces the counter-cyclicality of the dividend-price ratio's predictive power, and presents significantly lower out-of-sample forecast errors than the historical mean. Furthermore, the Bayesian estimation indicates that the persistence parameter of expected returns has been declining over the last few decades in tandem with the autocorrelation of realized returns.

Unbalanced Regressions and the Predictive Equation

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest an instrumental variable approach and discuss issues of validity and relevance. Applying the procedure to the prediction of daily returns on the S&P 500, our empirical analysis confirms return predictability and a positive risk-return trade-off.

The impact of volatility shifts on market efficiency: the case of four emerging Southeast Asian stock markets

The purpose of this paper is to analyse the behaviour of four Southeast Asian stock markets during the intervals of high uncertainties that accompany crises. Our analysis emphasises the effect of unexpected volatility shifts on market efficiency of the four emerging Southeast Asian markets over the past two decades. The purpose of this study is achieved through the iterated–cumulative–sum–of–squares–in–volatility (ICSS–EGARCH–M) model, a new approach in market efficiency studies. The empirical results of this study support rejection of the efficient market hypothesis for these markets, even when unexpected volatility shifts are integrated in the models. The results also provide significant empirical evidence for a positive risk–return tradeoff in the stock markets. Moreover, the stock markets are revealed to be more sensitive to global events than local. Except for the Philippines, asymmetrical responses to good and bad news are also part of the market behaviour for the markets.

Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?

This paper analyzes the risk–return trade-off in Europe using recent data from 11 European stock markets. After relaxing the linear assumptions in the risk–return relationship by introducing a new approach that considers the current state of the market, we obtain significant evidence for a positive risk–return trade-off for low volatility states. However, this finding is reduced or even non-significant during periods of high volatility. Maintaining the linear assumption over the risk–return trade-off leads to non-significant estimations for all cases. These results are robust across countries despite the conditional volatility model used. These results also demonstrate that the inconclusive results in previous studies may be due to strong linear assumptions when modeling the risk–return trade-off. This previous research fails to uncover the global behavior of the relationship between return and risk.