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The Cross-Section of Volatility and Expected Returns
TL;DR: This paper examined the pricing of aggregate volatility risk in the cross-section of stock returns and found that stocks with high sensitivities to innovations in aggregate volatility have low average returns, and that stock with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low return.
Abstract: We examine the pricing of aggregate volatility risk in the cross-section of stock returns Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns In addition, we find that stocks with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low average returns This phenomenon cannot be explained by exposure to aggregate volatility risk Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility
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TL;DR: This article showed that the association between future stock returns and information quality is negative (positive) for those firms with equity that is least (most) option-like, which is consistent with rational pricing, is predictable based on traditional asset pricing theory and is robust to numerous specifications.
Abstract: The association between future stock returns and information quality depends on how option-like is the firm's equity. The more growth options held by the firm, the more option-like is the firm's equity. This study shows that the association between future stock returns and information quality is negative (positive) for those firms with equity that is least (most) option-like. These results are consistent with rational pricing, are predictable based on traditional asset pricing theory, and are robust to numerous specifications. These findings offer a theoretical-based and empirically-supported explanation for why some influential prior studies, that do not condition on the option-like nature of equity, have documented either a positive or no association between information quality and future stock returns.
5 citations
TL;DR: In this paper, the authors examined the interaction between short-run return reversals, momentum and idiosyncratic volatility in the Australian market and found that stocks with high volatility earn low average returns over the next month.
Abstract: This paper examines the interaction between short-run return reversals, momentum and idiosyncratic volatility in the Australian market. We confirm that stocks with high idiosyncratic volatility earn low average returns over the next month. Unlike US studies which attribute this negative relation to short-run return reversals, we find that it is partly caused by a group of loser stocks (formed over one month) which continue to exhibit negative return drift (over the next month). We also investigate whether these stocks continue to display negative returns over longer horizons. In turn, we broaden our study to examine whether the momentum effect is persistent in stocks with high idiosyncratic volatility. We find that stocks with high idiosyncratic volatility realize significant abnormal momentum returns over a six-month holding period relative to stocks with low idiosyncratic volatility. As we document the persistence of momentum profits amongst stocks with high idiosyncratic volatility, our work contributes to the existing literature which suggests that idiosyncratic volatility is an important limit to the arbitrage of the momentum effect.
5 citations
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TL;DR: In this article, the impact of illiquidity both on market returns and on individual stock returns was investigated. And the results suggest that average market and individual stock return are a positive function of expected" illiquidities, while "unexpected" illiquity has a negative impact on contemporaneous returns.
Abstract: This paper examines whether illiquidity is a determinant of monthly stock returns in the German market. Estimating time-series and cross-sectional models, we investigate the impact of illiquidity both on market returns and on individual stock returns. Illiquidity is approximated by five measures that capture trading activity, trading costs and the price impact of order flow. Our results suggest that average market and individual stock returns are a positive function of "expected" illiquidity, while "unexpected" illiquidity has a negative impact on contemporaneous returns. Illiquidity should therefore be regarded as a determinant of returns for German stocks.
5 citations
TL;DR: This article analyzed the interaction between investor overconfidence, share turnover, return volatility and the disposition effect using posted price target updates retrieved from Yahoo! Finance online message boards and found that the overconfidence exhibited by posters is strongly associated with share turnover and return volatility.
Abstract: This paper analyzes the interaction between investor overconfidence, share turnover, return volatility and the disposition effect. I create a measure for investor overconfidence using posted price target updates retrieved from Yahoo!Finance online message boards. I find that posters are slow to adjust price targets to changes in underlying stock prices. These updating patterns appear to be suboptimal when contrasted with realized return and sell-side analyst benchmarks. I further find that the overconfidence exhibited by posters is strongly associated with share turnover, return volatility, and the magnitude of the disposition effect. The results in this paper add to our understanding of the link between behavioral biases and investor and stock-level anomalies in financial markets.
5 citations
TL;DR: This article examined how the thickness and width of the tails of return distributions affect expected returns and found that kurtosis is negatively related to expected returns. But they also found that the width of return distribution has a return premium that is substantially more negative than the thickness of the return distribution.
Abstract: This study examines how the thickness and width of the tails of return distributions affect expected returns. Contrary to the idea that thicker tails represent risk that is directly related to expected returns, we find that kurtosis is negatively related to expected returns. These results hold in a number of multifactor models and Fama-MacBeth (1973) regressions that include common controls. We find stronger evidence that the width of the return distribution has a return premium that is substantially more negative than kurtosis.
5 citations
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TL;DR: In this paper, the authors present some additional tests of the mean-variance formulation of the asset pricing model, which avoid some of the problems of earlier studies and provide additional insights into the nature of the structure of security returns.
Abstract: Considerable attention has recently been given to general equilibrium models of the pricing of capital assets Of these, perhaps the best known is the mean-variance formulation originally developed by Sharpe (1964) and Treynor (1961), and extended and clarified by Lintner (1965a; 1965b), Mossin (1966), Fama (1968a; 1968b), and Long (1972) In addition Treynor (1965), Sharpe (1966), and Jensen (1968; 1969) have developed portfolio evaluation models which are either based on this asset pricing model or bear a close relation to it In the development of the asset pricing model it is assumed that (1) all investors are single period risk-averse utility of terminal wealth maximizers and can choose among portfolios solely on the basis of mean and variance, (2) there are no taxes or transactions costs, (3) all investors have homogeneous views regarding the parameters of the joint probability distribution of all security returns, and (4) all investors can borrow and lend at a given riskless rate of interest The main result of the model is a statement of the relation between the expected risk premiums on individual assets and their "systematic risk" Our main purpose is to present some additional tests of this asset pricing model which avoid some of the problems of earlier studies and which, we believe, provide additional insights into the nature of the structure of security returns The evidence presented in Section II indicates the expected excess return on an asset is not strictly proportional to its B, and we believe that this evidence, coupled with that given in Section IV, is sufficiently strong to warrant rejection of the traditional form of the model given by (1) We then show in Section III how the cross-sectional tests are subject to measurement error bias, provide a solution to this problem through grouping procedures, and show how cross-sectional methods are relevant to testing the expanded two-factor form of the model We show in Section IV that the mean of the beta factor has had a positive trend over the period 1931-65 and was on the order of 10 to 13% per month in the two sample intervals we examined in the period 1948-65 This seems to have been significantly different from the average risk-free rate and indeed is roughly the same size as the average market return of 13 and 12% per month over the two sample intervals in this period This evidence seems to be sufficiently strong enough to warrant rejection of the traditional form of the model given by (1) In addition, the standard deviation of the beta factor over these two sample intervals was 20 and 22% per month, as compared with the standard deviation of the market factor of 36 and 38% per month Thus the beta factor seems to be an important determinant of security returns
2,899 citations
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TL;DR: In this paper, the generalized autoregressive conditionally heteroskedastic (GARCH) model of returns is modified to allow for volatility feedback effect, which amplifies large negative stock returns and dampens large positive returns, making stock returns negatively skewed and increasing the potential for large crashes.
Abstract: It is sometimes argued that an increase in stock market volatility raises required stock returns, and thus lowers stock prices. This paper modifies the generalized autoregressive conditionally heteroskedastic (GARCH) model of returns to allow for this volatility feedback effect. The resulting model is asymmetric, because volatility feedback amplifies large negative stock returns and dampens large positive returns, making stock returns negatively skewed and increasing the potential for large crashes. The model also implies that volatility feedback is more important when volatility is high. In U.S. monthly and daily data in the period 1926-88, the asymmetric model fits the data better than the standard GARCH model, accounting for almost half the skewness and excess kurtosis of standard monthly GARCH residuals. Estimated volatility discounts on the stock market range from 1% in normal times to 13% after the stock market crash of October 1987 and 25% in the early 1930's. However volatility feedback has little effect on the unconditional variance of stock returns.
1,793 citations
TL;DR: In this article, the authors examined a class of continuous-time models that incorporate jumps in returns and volatility, in addition to diffusive stochastic volatility, and developed a likelihood-based estimation strategy and provided estimates of model parameters, spot volatility, jump times and jump sizes using both S&P 500 and Nasdaq 100 index returns.
Abstract: This paper examines a class of continuous-time models that incorporate jumps in returns and volatility, in addition to diffusive stochastic volatility. We develop a likelihood-based estimation strategy and provide estimates of model parameters, spot volatility, jump times and jump sizes using both S&P 500 and Nasdaq 100 index returns. Estimates of jumps times, jump sizes and volatility are particularly useful for disentangling the dynamic effects of these factors during periods of market stress, such as those in 1987, 1997 and 1998. Using both formal and informal diagnostics, we find strong evidence for jumps in volatility, even after accounting for jumps in returns. We use implied volatility curves computed from option prices to judge the economic differences between the models. Finally, we evaluate the impact of estimation risk on option prices and find that the uncertainty in estimating the parameters and the spot volatility has important, though very different, effects on option prices.
1,040 citations
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TL;DR: In this article, a new way to generalize the insights of static asset pricing theory to a multi-period setting is proposed, which uses a loglinear approximation to the budget constraint to substitute out consumption from a standard intertemporal asset pricing model.
Abstract: This paper proposes a new way to generalize the insights of static asset pricing theory to a multi-period setting. The paper uses a loglinear approximation to the budget constraint to substitute out consumption from a standard intertemporal asset pricing model. In a homoskedastic lognormal selling, the consumption-wealth ratio is shown to depend on the elasticity of intertemporal substitution in consumption, while asset risk premia are determined by the coefficient of relative risk aversion. Risk premia are related to the covariances of asset returns with the market return and with news about the discounted value of all future market returns.
805 citations
TL;DR: This article investigated whether market-wide liquidity is a state variable important for asset pricing and found that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity.
Abstract: This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average return on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market return as well as size, value, and momentum factors.
789 citations