Positive Risk-return Relationship Research Articles

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

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.

Economic policy uncertainty, risk and stock returns: Evidence from G7 stock markets

• This paper examines uncertainties and risks on excess stock returns G7 markets using monthly observations. • The estimated results find evidence supporting positive risk-return relation not only for risk as measured by conditional volatility but also for downside risk. • The stock returns are negatively correlated with economic policy uncertainty innovation (ΔEPU) in both local and global sources. • In addition to the negative effect of ΔEPU on excess stock returns, evidence also demonstrates that lagged ΔEPU positively contributes to a rise in stock return volatility. • The conventional test of risk-return relation without controlling ΔEPU is subject to a specification bias.

Macroeconomic Sensitivity, Risk-Return Trade-Off and Volatility Dynamics Evidence From Developed and Developing Markets

This study aims to examine the impact of macroeconomic factors on the stock return volatility along with the pricing of risk, and asymmetry and leverage effect on a comparative basis for the USA and UAE markets. Further, these three dimensions are also investigated with regard to various firm's features (such as firm's size and age). The daily data for the period 4th January 2010 to 29th December 2017 of firm stock returns from the New York Stock Exchange (NYSE), the Abu Dhabi Securities Exchange (ADSE), and the Dubai Financial Market (DFM) is considered and three time-series models were applied. The results from GARCH (1. 1) indicated that all the economic factors have significant impact on the stock return volatility in both the markets. Similarly, the study also found evidence of asymmetry & leverage effect using EGARCH in the NYSE (for all firms) and the UAE (partially). Finally, for a majority of the firms, a positive risk-return relationship is found in the UAE and a negative risk-return relationship is found in the NYSE using GARCH-in the mean. Interestingly, these results in context of both markets were different with respect to various firm features such as firm size and age. In light of these results, it is concluded that both the markets have different dynamics with regard to all three dimensions. Hence, the investors have a clear opportunity to diversify their risk and investments across developed and emerging markets.

The intertemporal risk-Return relation, investor behavior, and technical trading profits: evidence from the G-7 countries

ABSTRACTThe intertemporal risk-return relation and investor behavior are both important pricing factors that jointly determine the expected market risk premium. Using the price adjustment process as a control variable, we find that the intertemporal risk-return relation is positive conditional on bad market news, but is non-positive conditional on good market news. This implies that good (bad) market news weakens (strengthens) the positive risk-return relation. The pattern in the distortion of the risk-return relation is consistent with short-term mispricing in which investors overvalue (undervalue) the stock market in reaction to good (bad) market news. We also show that ignoring the price adjustment process in the estimation of the risk-return relation leads to model misspecification and induces an upward (downward) bias in estimates of the relative risk aversion parameter conditional on good (bad) news. Our model of the asymmetric risk-return relation along with the price adjustment process is capable of generating the return dynamics that is attributable to technical trading profits. We suggest that the profitability of technical trading rules is not a violation of market efficiency, but a consequence of trading rules exploiting the asymmetric effect of price changes on the risk-return relation, along with the persistence property of price changes.

Multi-Factor Asset-Pricing Models under Markov Regime Switches: Evidence from the Chinese Stock Market

This paper proposes a Markov regime-switching asset-pricing model and investigates the asymmetric risk-return relationship under different regimes for the Chinese stock market. It was found that the Chinese stock market has two significant regimes: a persistent bear market and a bull market. In regime 1, the risk premiums on common risk factors were relatively higher and consistent with the hypothesis that investors require more compensation for taking the same amount of risks in a bear regime when there is a higher risk-aversion level. Moreover, return dispersions among the Fama–French 25 portfolios were captured by the beta patterns from our proposed Markov regime-switching Fama–French three-factor model, implying that a positive risk-return relationship holds in regime 1. On the contrary, in regime 2, when lower risk premiums could be observed, portfolios with a big size or low book-to-market ratio undertook higher risk loadings, implying that the stocks that used to be known as “good” stocks were much riskier in a bull market. Thus, a risk-return relationship followed other patterns in this period.

Intraday and interday distribution of stock returns and their asymmetric conditional volatility: Firm-level evidence

This paper is a pioneering effort to jointly analyze the intraday and interday distribution of stock returns and their asymmetric time varying volatility using firm level data from an emerging stock market, namely, the Bourse Istanbul. The joint intraday and interday distribution analysis is based on the two trading sessions with a two-hour lunch break and the trading days of week. The asymmetric time varying volatility is based on the Threshold Generalized Autoregressive Conditional Heteroscedasticity-in-Mean [TGARCH(1,1)-M] model with systematic risk entering the return generating process. This is a unique framework to simultaneously test (i) the weak-form informational efficiency, (ii) the conditional total risk-return relationship and the systematic risk effects, and (iii) volatility persistence and asymmetry in volatility. The firm level intraday data sets are used for the period 1995 to 2015. Firstly, a strong result can be pronounced for a positive return effect for the second trading session of Thursdays followed by the second session of Fridays. The first session of Wednesdays is more associated with a stock price decline. The volatility is the highest in the second session of Mondays. Although the second session of Thursdays sees a volatility increase referring to a positive risk-return relationship, Fridays do not have any significant volatility increase. This can be seen as a true anomaly where we could observe higher returns with lower volatility. Secondly, it can be concluded that only the systematic risk is priced for the great majority of the selected firms. In addition, we cannot observe a significant asymmetry in the volatility in most cases. Finally, it is found that the financial companies have a significantly higher systematic risk than the industrial companies.

An examination of the risk-return relation in the Australian housing market

PurposeExtensive studies have investigated the relation between risk and return in the stock and major asset markets, whereas little studies have been done for housing, particularly the Australian housing market. This study aims to determine the relationship between housing risk and housing return in Australia.Design/methodology/approachThe analysis of this study involves two stages. The first stage is to estimate the presence of volatility clustering effects. Thereafter, the relation between risk and return in the Australian housing market is assessed by using a component generalised autoregressive conditional heteroscedasticity-in-mean (C-CARCH-M) model.FindingsThe empirical results show that there is a strong positive risk-return relationship in all Australian housing markets. Specifically, comparable results are also evident in all housing markets in various Australian capital cities, reflecting that Australian home buyers, in general, are risk reverse and require a premium for higher risk level. This could be attributed the unique characteristics of the Australian housing market. In addition, there is evidence to suggest that a stronger volatility clustering effect than previously documented in the daily case.Practical implicationsThe findings enable more informed and practical investment decision-making regarding the relation between housing return and housing risk.Originality/valueThis paper is the first study to offer empirical evidence of the risk-return relationship in the Australian housing market. Besides, this is the first housing price volatility study that utilizes daily data.

Estimating the Risk-Return Relation in the Korean Stock Market

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.

Risk–Return Relationship in BRIC Equity Markets: Evidence from Markov Regime Switching Model with Time-varying Transition Probabilities

A rich literature supports the existence of both positive and negative relationship between the risk and return in the developed equity markets. However, the present study attempts to capture the risk–return relationship in the most promising and opportunities-instilled emerging market club, the “BRIC” equity markets, by employing a Markov regime switching model with time-varying transition probabilities, further taking St. Louis Fed Financial Stress Index (the US financial market stress) as an economic variable. The weekly benchmark index values are used in the analysis, spanning from the year 2004 to 2013. The results report the existence of time-varying transition probabilities with respect to the Brazilian and Indian markets only and fixed transition probabilities for the other countries undertaken. The Markov results support the existence of two regimes, wherein regime-1 reports a positive risk–return relationship, and regime-2 reports a negative relationship between the risk and return. Ironically, the Chinese equity market is found to be the riskiest but a perfect hedge instrument amongst others, considering its risk–return interactions in both the regimes. Furthermore, a lower level of financial stress in the US financial market is associated with a higher probability of remaining in the “Bullish” regime-1 in the Indian market as well as Brazilian market. Moreover, there is a positive co-movement between the US financial stress and the expected time-varying duration of remaining in the “Bearish” regime. This shows that due to the growing interdependence among the worldwide economies, a financial stress in one economy does have an impact on the other markets and risk–return relationship in their equity markets. An understanding of the risk–return dynamics coupled with the impact of exogenous variables is an imperative task that a portfolio manager must undertake so as to justify and manage the investments made in the equity markets.

Risk and the Volatility Anomaly

Is the long-term volatility anomaly a function of risk measure choice? Using such symmetric risk measures as beta and variance, the volatility anomaly is apparent in the United States and 21 other global equity markets. This study finds less support for the volatility anomaly when symmetric risk measures are replaced with their asymmetric risk counterparts: relative variance (negative semivariance minus variance) and relative beta (downside beta minus beta). When relative variance is the sorting variable, the expected positive risk–return relationship is restored. Strategies of buying low-variance or low-beta stocks and shorting high-variance or high-beta stocks generate positive monthly risk-adjusted returns in nearly one-third of the sample. These results reverse or disappear, however, when relative variance or relative beta are the sorting variables. Portfolio matching shows that portfolios sorted on variance versus relative variance are truly different portfolios, and the same result holds true for portfolios sorted on beta versus relative beta.

A Comparative Study of the Taiwan and Japan Equity and Foreign Exchange Markets: Modeling, Estimation and Application of the Component Garch-in-Mean Model

The main purpose of this paper is to verify the effectiveness of the bivariate Component GARCH-in-mean (GARCH-M) model and analyze the interactions and risk premium of equity markets by exploring the short- and long-run volatility components on both the Taiwanese and Japanese equity markets. We show that unexpected shocks of volatility will in general influence the fluctuations of both equity and foreign exchange markets. Persistence on the long-run volatility components of both markets is also found. The results also reveal that the positive risk-return relation on equity markets can be further verified when the impacts of short and long-run volatility components are decomposed by the Component GARCH-M model. The decomposition can also facilitate reflecting the transitory and permanent volatility impacts of foreign exchange exposure on the returns of equity markets.

BSE SME Equity Financing Platform: A study of Index Risk-Return Paradox

The study attempts to introduce an innovative initiative of the BSE's SME equity financing segment and explore the time varying risk-return relationship of the BSE SME IPO index by employing GARCH-M, TGARCH-M, EGARCH-M and Asymmetric CGARCH models. The results spotlight the existence of a positive risk-return relationship between short run volatility component and the actual returns, whereas a negative relationship has been observed between the long run volatility and index returns. The results reported by the GARCH-M, EGARCH-M and TGARCH-M model in the context of the BSE SME IPO index shows insignificant, but a positive relationship between the risk and return. A positive shock has a more pronounced impact on volatility in case of the BSE SME IPO segment. A study relating to the relationship between the index risk and returns is an imperative task to be performed by the existing and prospective investors.

RELATION BETWEEN RISK AND RETURN IN TUNISIAN’S STOCK MARKET AFTER THE REVOLUTION (DURING POLITICAL INSTABILITY)

This paper examines the conditional relationship between the Tunisian stock market performance and the various sources of risk (market risk, the risk of oil prices, exchange rate risk, skewness and kurtosis) after the revolution (2011-2014). The methodology used in this paper is a multi-factor model to analyze the risk-return relationship for most equity sectors. We find a positive risk-return relationship statistically significant at a 1% in the up and down market. The oil price is found to be negative and statistically insignificant in the up and down oil market, suggesting that the oil price is indeed an important factor in determining stock returns. Results for other risk factors like skewness and kurtosis are also presented. These results are useful for individual and institutional investors, managers and policy makers.Keywords: Risk; return; multifactor conditional model; Tunisian Stock Market; instability political.

Extreme Downside Risk and Financial Crises

We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali, Demirtas, and Levy (2009) with a Markov switching mechanism. We show that the risk-return relationship identified by Bali, Demirtas, and Levy (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high-volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship under all states of market volatility.

Effect of Volatility Changes on Emerging Stock Markets: The Case of Jordan

<p>The Middle East financial markets have experienced several unexpected volatility shifts during the last two decades had recorded a serious impact on these markets and caused a financial turmoil that has elevated the uncertainties in the region. In view of this, more empirical findings should be learned and documented for future benefits. As one of the affected countries, Jordan was chosen as a case to provide empirical insight on the matter. This paper analyzed the behavior of Jordan’s stock market (Amman Stock Exchange, ASE) during the intervals of high uncertainty. It highlighted the impact of volatility on this market in terms of its efficiency and returns, during 2004-2012 periods, by utilizing the iterated cumulative sums of squares (ICSS) algorithm, GARCH and GARCH-M models. Sudden changes in volatility seem to arise from the evolution of emerging stock markets, exchange rate policy changes and financial crises. Evidence also reveals that when sudden shifts are taken into account in the GARCH models, the persistence of volatility is reduced significantly in every series. Research results provided significant empirical evidence for positive risk-return relationship in the stock exchange. Moreover, this study also found that the stock market, across all sectors, was more sensitive to global news events as compared to the local events. The asymmetrical responses to good and bad news were also an important characteristic of the ASE market behavior.</p>

The intertemporal risk-return relationship: Evidence from international markets

This paper examines the intertemporal capital asset pricing (Merton, 1973) for industry portfolio returns of 14 international markets. Using different multivariate GARCH models to estimate time-varying conditional covariances between industry excess returns and market excess returns by controlling for financial market volatility variables and the Fama–French–Carhart factors, we find positive evidence to support the tradeoff between industry excess return and the covariance risk for all advanced markets (except Germany), all Asian markets, and Argentina in Latin American markets. The evidence suggests that the positive risk-return relationship is more pronounced during the tranquil period.

Risk-return trade-off and serial correlation: Do volume and volatility matter?

I investigate a relation between the conditional mean and variance of the aggregate stock return using a model that allows the relevance of the risk-return trade-off and autocorrelation to change over time. The model detects a positive risk-return relation, but the importance of the risk-return relation fluctuates with the level of information flow, measured by volatility. During low-volatility periods, market-wide persistence in returns increases, leading to a failure of the pure risk-return explanation for expected returns. This offers an explanation as to why detection of a positive risk-return trade-off has been challenging, while autocorrelation has been a robust finding.

Further Evidence on the Validity of CAPM: the Istanbul Stock Exchange Application

As one of the most important models in the finance literature, the Capital Asset Pricing Model (CAPM) assumes the existence of a positive and linear relationship between the systematic risk and required rates of return on stocks. The model is extensively researched in the academia and frequently used in business world since its development half a century ago. Its popularity comes from the simplification it provides for the complex process of asset pricing by making the assumption that only one single factor affects stock returns. But, as this is an unrealistic assumption, the validity of the model in its standard (unconditional) form is repeatedly rejected by empirical tests. Pettengill et al. (1995) developed an alternative conditional CAPM approach where the standard model is improved by taking bull and bear market conditions into consideration. According to this model, there is a positive (negative) risk-return relationship during up (down) market periods. Using this reasoning, Pettengill et al. (1995) tested up and down market periods separately and reached highly significant results that support CAPM. In this study, both the unconditional and conditional versions of CAPM are tested in the Istanbul Stock Exchange (ISE) for the period of nine years from 2003 to 2011. The test period is divided into four sub-periods. The unconditional CAPM is rejected for the sample period. A result of the conditional test shows that there is a statistically significant conditional relationship during some sub-periods. However, since the risk-return relationship in up and down markets is not symmetric, this conditional relationship does not indicate a positive risk-return tradeoff. Thus, CAPM may not be a useful asset pricing model for the ISE. DOI: http://dx.doi.org/10.5755/j01.ee.25.1.1847

Risk–return trade-off in the pacific basin equity markets

We conduct an empirical study of risk–return trade-off in fourteen Pacific basin equity markets using several volatility estimators, including five variants of GARCH class, equally weighted rolling window volatility, and mixed data sampling (MIDAS), as well as binormal GARCH (BiN-GARCH) model which allows for non-zero conditional skewness in returns. Our findings imply that the BiN-GARCH model, which allows for time-variation in the conditional skewness and market price of risk, captures the expected positive risk–return relationship in eleven out of fourteen markets studied. In comparison, symmetric skewness models such as MIDAS or GARCH variants fail to capture positive and statistically significant market price of risk estimates. These results provide support for the growing literature on the necessity of modeling conditional higher moments in financial research.

Intraday price dynamics in spot and derivatives markets

This study examines intraday relationships among the spot index, index futures, and the implied volatility index based on the VAR(1)-asymmetric BEKK-MGARCH model. Analysis of a high-frequency dataset from the Korean financial market confirms that there is a strong intraday market linkage between the spot index, KOSPI200 futures, and VKOSPI and that asymmetric volatility behaviour is clearly present in the Korean market. The empirical results indicate that the futures return shock affects the spot market more severely than the spot return shock affects the futures market, though there is a bi-directional causal relationship between the spot and futures markets. Our results, based on a high-quality intraday dataset, satisfy both the positive risk–return relationship and asymmetric volatility effect, which are not reconciled in the frameworks of previous studies.