Distribution Of Equity Returns Research Articles

Higher-order comoment contagion among G20 equity markets during the COVID-19 pandemic

We study the distribution of equity returns in the G20 equity markets to test for contagion following the first official report of a COVID-19 case in China in December 2019 and the subsequent announcement of a global pandemic in March 2020. We find evidence of contagion through equity market tail risk in early 2020 followed by widespread evidence of contagion across multiple channels from the U.S. to G20 equity markets after the pandemic announcement. Our results suggest that global equity markets may be exposed to unpriced pandemic risk factors with implications for portfolio diversification, risk management and financial stability.

Perceptions of the threat to national security and the stock market

This paper investigates the impact of national security concerns on equity valuations in the U.S. from 1985 to 2018. While existing literature focuses on actual events of a violent nature, our process is different. We examine the effect of continuous indices derived from media coverage of these issues. Our analysis documents that journalistic reportage can provoke significant changes in the first and, to a certain extent, the second moment of U.S. equity return distribution. These changes extend even beyond the impact of events that transpired. Furthermore, business confidence also appears to be undermined by media coverage of threats to the nation and its citizenry.

Higher-order Comoment Contagion Among G20 Equity Markets During the COVID-19 Pandemic

We study the distribution of equity returns in the G20 equity markets to test for contagion following the first official report of a COVID19 case in China in December 2019 and the subsequent announcement of a global pandemic in March 2020. We find evidence of contagion of Chinese equity market tail risk in early 2020 followed by widespread evidence of contagion across multiple channels from the U.S. to G20 equity markets after the pandemic announcement. Our results suggest that global equity markets may be exposed to unpriced pandemic risk factors with implications for portfolio diversification, risk management and financial stability.

Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework

A key question for understanding the cross-section of expected returns of equities is the following: which factors, from a given collection of factors, are risk factors, equivalently, which factors are in the stochastic discount factor (SDF)? Though the SDF is unobserved, assumptions about which factors (from the available set of factors) are in the SDF restricts the joint distribution of factors in specific ways, as a consequence of the economic theory of asset pricing. A different starting collection of factors that go into the SDF leads to a different set of restrictions on the joint distribution of factors. The conditional distribution of equity returns has the same restricted form, regardless of what is assumed about the factors in the SDF, as long as the factors are traded, and hence the distribution of asset returns is irrelevant for isolating the risk-factors. The restricted factors models are distinct (nonnested) and do not arise by omitting or including a variable from a full model, thus precluding analysis by standard statistical variable selection methods, such as those based on the lasso and its variants. Instead, we develop what we call a Bayesian model scan strategy in which each factor is allowed to enter or not enter the SDF and the resulting restricted models (of which there are 114,674 in our empirical study) are simultaneously confronted with the data. We use a Student-t distribution for the factors, and model-specific independent Student-t distribution for the location parameters, a training sample to fix prior locations, and a creative way to arrive at the joint distribution of several other model-specific parameters from a single prior distribution. This allows our method to be essentially a scaleable and tuned-black-box method that can be applied across our large model space with little to no user-intervention. The model marginal likelihoods, and implied posterior model probabilities, are compared with the prior probability of 1/114,674 of each model to find the best-supported model, and thus the factors most likely to be in the SDF. We provide detailed simulation evidence about the high finite-sample accuracy of the method. Our empirical study with 13 leading factors reveals that the highest marginal likelihood model is a Student-t distributed factor model with 5 degrees of freedom and 8 risk factors.

Perceptions of the Threat to National Security and the Stock Market

This paper investigates the impact of national security concerns on equity valuations in the U.S. from 1985 to 2018. While existing literature focuses on actual events of a violent nature, our process is different. We examine the effect of continuous indices derived from media coverage of these issues. Our analysis documents that journalistic reportage can provoke significant changes in the first and, to a certain extent, the second moment of U.S. equity return distribution. These changes extend even beyond the impact of events that transpired. Furthermore, business confidence also appears to be undermined by media coverage of threats to the nation and its citizenry.

Low-risk equity investment – From theory to practice

Financial theory assumes that higher risk is compensated on average by higher returns. However, the outperformance of low-volatility stocks during the last 50 years has been among the most puzzling anomalies in equity markets. At the same time, low-risk investing has recently gained a remarkable interest, due to its documented performance coupled with the unprecedented volatility experienced during the last global financial crisis. The section ‘Literature Review’ of our study discusses how researchers have been documenting such anomaly and explaining (since the early nineties) this phenomenon with theories referring to leverage constraints, behavioral bias, delegated portfolio, benchmarking and the utility function of fund managers. Recognizing that most of the available empirical research focuses on the US equity market, section ‘Empirical Evidence in the International Arena’ is dedicated to some additional tests that we have run on the persistency and significance of the anomaly in the international arena: We find that the relationship between risk-adjusted returns and risk is negative indeed, and that the relationship holds regardless of the risk measure employed for tests (Beta or volatility). In section ‘Can Skewness And Convexity Explain the Low-Risk Anomaly?’, we investigate two possible explanations of the anomaly related to the distribution of equity returns, namely skewness and convexity. We find that high-risk stocks exhibit higher skewness and higher convexity than low-risk stocks. These results may effectively explain the anomaly because, if the price paid for stocks with higher-than-average skewness and convexity is inflated, their subsequent returns are consequently lower-than-average, at least in risk-adjusted terms. In the last section of our research, we discuss two popular risk-based smart beta strategies (minimum variance and risk parity) proving that both of them benefit from the low-risk anomaly via a significant and systematic bias toward low-risk stocks. We ultimately discuss some best practices of implementing the minimum-variance process, and provide an effective and parsimonious portfolio construction rule for the risk parity process.

Study of Behavior of Equity Returns in India

This paper examines the behavior of equity returns in India for the period from 1999-2009. The empirical tests on the data of twenty Indian stocks actively traded in the Bombay Stock Exchange leads to the findings that the stock returns were highly volatile. The presence of skewness and kurtosis of leptokurtic nature provides further evidences for the asymmetrical distribution of equity returns. Stocks bear less share of market risk and behave idiosyncratically.

Modelling dependence structure with Archimedean copulas and applications to the iTraxx CDS index

In this paper we model the dependence structure between credit default swap (CDS) and jump risk using Archimedean copulas. The paper models and estimates the different relationships that can exist in different ranges of behaviour. It studies the bivariate distributions of CDS index spreads and the kurtosis of equity return distribution. To take into account nonlinear relationships and different structures of dependency, we employ three Archimedean copula functions: Gumbel, Clayton, and Frank. We adopt nonparametric estimation of copula parameters and we find an extreme co-movement of CDS and stock market conditions. In addition, tail dependence indicates the extreme co-movements and the potential for a simultaneous large loss in stock markets and a significant default risk. Ignoring the tail dependence would lead to underestimation of the default risk premium.

Tests of the performance of structural models in bankruptcy prediction

In this study we investigate the relative performance of six structural credit risk models in predicting bankruptcy. The six models studied (the Merton, Black-Cox, Leland-Toft, Longstaff-Schwartz, flat barrier and Geske models) cover nested assumptions, thereby allowing us to study the factors that affect the accuracy of bankruptcy prediction. Using data from the period 1983-2002 (prior to the Sarbanes-Oxley Act), we compare the performance of these models for various prediction horizons and identify the factors that have the most substantial prediction power of default. Our results suggest that the left-tail probability of equity return distribution plays the most crucial role in predicting default. Moreover, we find that recovery boundary and multi-period default are important factors, while random interest rates are not.

How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution?

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The empirical results strongly reject ignoring structural change or using a fixed-length moving window. The shape of the long-run distribution is affected by breaks, which has implications for risk management and long-run investment decisions.

Estimating the Accuracy of Value-at-Risk (VAR) in Measuring Risk in Equity Investment in India

Abstract Over the past few years, the Value-at-Risk (VaR) has become a standard measure of market risk embraced by banks, trading firms, mutual funds and others, including even the non financial firms. But any risk measure is useful and reliable only insofar as it can be verified for its accuracy. This paper attempts to evaluate the accuracy of VaR in estimating the risk in equity investment in India. For this purpose we have used daily data for 30 securities comprising BSE-Sensex and two major stock indices- BSE Sensex and NSE Nifty for the period January 2006 to February 2007 and portfolio-normal method (parametric approach to VaR calculation) for calculation of VaR. The hypothesis regarding accuracy of VaR estimates has been tested using Chi-square test. The results show that VaR estimate does not accurately measure the risk in equity investment in India as VaR overestimates the loss in 24 securities out of 30 securities. It is only in case of 4 securities that the observed number of violations is exactly equal to the expected number. These results may be attributed to non-normal distribution of equity returns in Indian securities market as against the normally distributed returns assumed under portfolio-normal method. All the securities are showing excess kurtosis estimate, exhibiting the leptokurtic returns’ distribution and also, out of 30 securities, 20 are showing negatively skewed returns and 10 are showing positively skewed returns. Moreover the assumption of past representing the future is also not validated in the present case in the context of stock volatility observed during the period. We have also observed that portfolio- normal method of VaR computation is a better risk measure for estimating portfolio risk as compared to risk on individual securities.

Herding Behaviour by Equity Foreign Investors on Emerging Markets

This article analyses empirically the herding behaviour on emerging markets, measuring the degree of herding by foreign investors on emerging equity markets, and evaluating the effects of this behaviour on the volatility and kurtosis of the markets. We use an adaptation of the LSV Herding measure, calculating the measure for a sample of nine emerging markets over the period 2000-2005. Our overall mean, although is lower than previous studies with emerging equity markets during the late 1990s, still indicates the presence of herding behaviour. Regarding the effects of Herding on the risk measures, our results are mixed. We found no effect of the Herding on the volatility. However, we found evidence that fat tails of equity return's distribution may be caused by this herding behaviour of foreigners.

Temporal aggregation of equity return time-series models

With large volatility observed in stock markets around the world over the last few years, many actuaries are now being urged to employ stochastic models to measure the solvency risk generated from insurance products with equity-linked guarantees. There are a large number of potential stochastic models for equity returns. Insurance regulators, both in Europe and North America, normally do not restrict the use of any stochastic model that reasonably fits the historical baseline data. However, in the U.S. and Canada, the final model must be calibrated to some specified distribution percentiles. The emphasis of the calibration process remains on the tails of the equity return distribution over different holding periods. In this paper, we examine the effect of temporal aggregation on classes of stochastic equity return models that are commonly used in actuarial practice. The advantages of choosing a closed (under temporal aggregation) class of processes for modelling asset returns and equity-linked guarantees are discussed. Actuarial applications of temporal aggregation using S&P500 data are given.

How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution?

We provide an approach to forecasting the long-run (unconditional distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The paper illustrates the importance of uncertainty about structural breaks and the value of modeling higher-order moments of excess returns when forecasting the return distribution and its moments. The shape of the long-run distribution and the dynamics of the higher-order moments are quite different from those generated by forecasts which cannot capture structural breaks. The empirical results strongly reject ignoring structural change in favor of our forecasts which weight historical data to accommodate uncertainty about structural breaks. We also strongly reject the common practice of using a fixed-length moving window. These differences in long-run forecasts have implications for many financial decisions, particularly for risk management and long-run investment decisions.

Dependence structure between the credit default swap return and the kurtosis of the equity return distribution: Evidence from Japan

We examine the dependence structure between the credit default swap (CDS) return and the kurtosis of the corresponding equity return distribution using copula functions to specify its nonnormal and nonlinear relationship. Three candidates, the Gaussian, the Student's t, and the Gumbel copulas, are compared. Daily CDS rates of 46 reference entities are collected from the Tokyo International Financial Exchange covering the period from April 2004 to June 2005. Our empirical results suggest that in lower rating classes, the Gumbel copula is the best fitting model, followed by the Student's t. The dependence structure is positive and asymmetric. To compensate for the higher risk, possibly incurred by more jumps, protection sellers demand higher CDS returns. Meanwhile, the upper tail dependence becomes significant as jump events and CDS returns increase simultaneously. Finally, CDS returns in lower rating classes are more sensitive to jump risk than those in the higher ratings.

Explaining the Level of Credit Spreads: Option-Implied Jump Risk Premia in a Firm Value Model

Prices of equity index put options contain information on the price of systematic downward jump risk. We use a structural jump-diffusion firm value model to assess the level of credit spreads that is generated by option-implied jump risk premia. In our compound option pricing model, an equity index option is an option on a portfolio of call options on the underlying firm values. We calibrate the model parameters to historical information on default risk, the equity premium and equity return distribution, and S&P 500 index option prices. Our results show that a model without jumps fails to fit the equity return distribution and option prices, and generates a low out-of-sample prediction for credit spreads. Adding jumps and jump risk premia improves the fit of the model in terms of equity and option characteristics considerably and brings predicted credit spread levels much closer to observed levels.

Explaining the Level of Credit Spreads: Option-Implied Jump Risk Premia in a Firm Value Model

Prices of equity index put options contain information on the price of systematic downward jump risk. We use a structural jump-diffusion firm value model to assess the level of credit spreads that is generated by option-implied jump risk premia. In our compound option pricing model, an equity index option is an option on a portfolio of call options on the underlying firm values. We calibrate the model parameters to historical information on default risk, the equity premium and equity return distribution, and S&P 500 index option prices. Our results show that a model without jumps fails to fit the equity return distribution and option prices, and generates a low out-of-sample prediction for credit spreads. Adding jumps and jump risk premia improves the fit of the model in terms of equity and option characteristics considerably and brings predicted credit spread levels much closer to observed levels.

Institutional Ownership and Distribution of Equity Returns

AbstractAlthough there is considerable evidence of the importance of skewness and kurtosis in equity returns, much less attention has been paid to their determinants. Recent theoretical and empirical advances in the literature suggest that the information structure and other market characteristics affect the nature of return distributions. One such characteristic is the degree of institutional ownership in the stock. This study hypothesizes and documents a significant inverse relationship between the degree of institutional ownership and the standard deviation, skewness, and kurtosis of equity returns.

INSTITUTIONAL OWNERSHIP AND DISTRIBUTION OF EQUITY RETURNS

Financial ReviewVolume 22, Issue 3 p. 14-14 INSTITUTIONAL OWNERSHIP AND DISTRIBUTION OF EQUITY RETURNS R. Aggarwal, R. Aggarwal The University of ToledoSearch for more papers by this authorR. Rao, R. Rao The University of ToledoSearch for more papers by this author R. Aggarwal, R. Aggarwal The University of ToledoSearch for more papers by this authorR. Rao, R. Rao The University of ToledoSearch for more papers by this author First published: August 1987 https://doi.org/10.1111/j.1540-6288.1987.tb01148.xCitations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume22, Issue3August 1987Pages 14-14 RelatedInformation