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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: In this paper, the authors show that the relationship between sensitivity to changes in aggregate volatility and expected return on stocks documented by Ang et al. (2006) for the fifteen-year period from 1986 to 2000 have disappeared in the following fifteen years period.
Abstract: This paper shows that the relationships between sensitivity to changes in aggregate volatility and expected return on stocks documented by Ang et al. (2006) for the fifteen-year period from 1986 to 2000 have disappeared in the following fifteen-year period. Aggregate volatility betas in the portfolio pre-formation month have not predicted post-formation returns. Alphas from time-series regressions of excess returns on the high-minus-low sensitivity to aggregate volatility portfolio with respect to the CAPM, the Fama-French 3-factor model, and the Fama-French 5-factor model have not been statistically different from zero. Finally, the price of aggregate volatility risk has not been statistically different from zero. These findings raise an important question of whether the empirical results on the relationships between sensitivity to changes in aggregate volatility and expected stock returns in Ang et al. (2006) held based on rational expectations or due to mispricing. Additionally, I present evidence that the price of aggregate volatility risk may be asymmetric.
1 citations
TL;DR: This paper examined the role of liquidity risk in explaining the relationship between asset size and hedge fund performance using data from the Lipper TASS hedge fund database over 1994-2012, and found that large funds are less able to recover from the more significant losses incurred during market-wide liquidity crises, resulting in lower performance for large funds relative to small funds.
Abstract: Using data from the Lipper TASS hedge fund database over 1994-2012, we examine the role of liquidity risk in explaining the relationship between asset size and hedge fund performance While a significant negative size-performance relationship exists for all hedge funds, once we stratify our sample by liquidity risk, we find that such a relationship only exists among funds with the highest liquidity risk This result cannot be explained away by the liquidity hypothesis or the leverage effect Liquidity risk is found to be another important source of diseconomies of scale in the hedge fund industry Evidently, for high liquidity risk funds, large funds are less able to recover from the more significant losses incurred during market-wide liquidity crises, resulting in lower performance for large funds relative to small funds
1 citations
TL;DR: In this article, the authors used the size effect adjusted institutional ownership as a proxy for institutional limits to arbitrage and found that mandated and financial constrained institutional investors contribute positively to the low beta anomaly but mitigate the low volatility anomaly using sorting and Fama-MacBeth regressions.
Abstract: Institutional investors subject to benchmarking, short-selling and leverage constraints have asymmetric effects on both low beta and low volatility anomalies documented by previous studies. Specifically, institutional investors prefer high-beta stocks to low-beta stocks to minimize the tracking error and utilize the embedded leverage of high beta stocks, leading to low-beta anomaly. They can act as the supply source of security lending to the short-sellers, mitigating the overpricing induced negative effect on expected returns from idiosyncratic volatility. Using size effect adjusted institutional ownership as a proxy for institutional limits to arbitrage, I confirm that mandated and financial constrained institutional investors contribute positively to the low beta anomaly but mitigate the low IVOL anomaly using sorting and Fama-MacBeth regressions. I distinguish the highly correlated low beta and low volatility anomalies and find a significantly positive risk premium for institutional holding. A strong January reversal effect of idiosyncratic volatility on expected return is also documented.
1 citations
TL;DR: In this paper, the relationship between risk and the use of Taxable REITs Subsidiary (TRS) is investigated and a unique data sample is employed to study the relationship.
Abstract: This paper employs a unique data sample to study the relationship between risk and the use of Taxable REITs Subsidiary (TRS). Total volatility is decomposed into systematic risk and idiosyncratic risk in order to examine whether cross-sectional variations in REITs risk are related to use of TRS as well as by investment sector. The relation between REITs risk and REITs liquidity is also explored in this paper by using three liquidity measures: Percentage spread, dollar volume and price impact. The evidence suggests that REITs with TRS (TRS-REITs) are more exposed – or sensitive – to systematic risk, however, they are more liquid than REITs that do not use TRS (Non-TRS-REITs). Even though the higher risk sensitivities and potential benefits that a TRS offers to a REIT, the TRS-REITs always perform poorer than Non-TRSREITs, the unique exception is during the years of recession and merely for EREITs. Therefore, and according to this paper’s findings, one can conclude that the decision to choose REITs to be included in an investment portfolio is not trivial. Investors should be aware that TRS-REITs and Non-TRS-REITs present differences in systematic risk, liquidity and performance. JEL Classifications: G10, G11, G12
1 citations
TL;DR: In this article, the authors investigate whether the lotto investor can benefit from the time-varying skewness of market portfolio and how to capture the gain using skew timing strategies.
Abstract: In this paper, we investigate whether the “lotto investor” can benefit from the time-varying skewness of market portfolio and how to capture the gain using skew timing strategies. We find that empirically applying the mean-variance-skewness (M-V-S) rule of Mitton and Vorkink (2007) generates similar performance as that of traditional mean-variance rule, because the optimal weight of M-V-S still mainly depends on the mean and variance unless the forecasted skewness is extremely large. To improve the M-V-S, we suggest combining two constrained versions of M-V-S, namely the mean-variance (M-V) and mean-skewness (M-S). Discarding the variance, the M-S fully considers the usefulness of skewness and the optimal weight solely depends on mean and skewness. However, M-S investor probably suffers huge loss due to forecasting errors. Combining the M-V with M-S should theoretically perform better than individual rules, and hence better than the M-V-S rule. Empirically, we find that the combination rule indeed generates superior performance, in terms of certainty equivalent returns, Sharpe ratios, and skewness of portfolio returns distribution.
1 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