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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 article, the authors demonstrate that ranked stocks underperform unranked stocks by over 1.50% during the month after the ranking, in line with attention-induced overpricing.
Abstract: One of the most salient events for a stock is being a daily winner or loser: these stocks are highlighted prominently in the media, leading to investor attention spikes. We demonstrate that ranked stocks underperform unranked stocks by over 1.50% during the month after the ranking, in line with attention-induced overpricing. To establish causality, we introduce an identification strategy that exploits unconventional return-measurement periods. We show that the underperformance of daily winners and losers provides a new attention-based solution for the idiosyncratic volatility puzzle and related return patterns, and thus a simple unifying explanation for important asset pricing anomalies.
25 citations
TL;DR: This article showed that stocks' exposure to oil volatility risk now drives the cross-section of expected returns and that the difference in average return between the quintile of stocks with low exposure and high exposure to volatility is significant at 066% per month.
Abstract: After the financialization of commodity futures markets in 2004-05 oil volatility has become a strong predictor of returns and volatility of the overall stock market Furthermore, stocks' exposure to oil volatility risk now drives the cross-section of expected returns The difference in average return between the quintile of stocks with low exposure and high exposure to oil volatility is significant at 066% per month, and oil volatility risk carries a significant risk premium of -060% per month In the post-financialization period, oil volatility risk is strongly related with various measures of funding liquidity constraints suggesting an economic channel for the effect
24 citations
TL;DR: In this article, the authors developed a method to extract only the priced factors from stock returns, including level, slope and curve factors, from stock portfolios, using multiple regression on anomaly characteristics to predict expected returns.
Abstract: I develop a method to extract only the priced factors from stock returns. First, I use multiple regression on anomaly characteristics to predict expected returns. Next, I form portfolios of stocks sorted by their expected returns. Then, I extract statistical factors from these sorts using principal components. The procedure isolates and emphasizes the comovement across assets that is related to expected returns as opposed to firm characteristics. The procedure produces level, slope and curve factors for stock returns. The factors perform better than the Fama and French (1993, 2014) three and five factor models and comparably to the four factor models of Carhart (1997), Novy-Marx (2013) and Hou, Xue, and Zhang (2012). Horse races show that other factors add little to the Level, Slope and Curve factors. The Level, Slope and Curve factors have macroeconomic interpretations. The factors capture strong variation in consumption growth across the sorted portfolios, and when embedded in an ICAPM, proxy for innovations to dividend yield, credit spread and stock volatility.
24 citations
TL;DR: In this article, the authors show that the explanatory power of the ICAPM application by Campbell and Vuolteenaho (2004) relies critically on the computation of Dimson (1979) covariances (betas).
Abstract: The Intertemporal CAPM (ICAPM) by Merton (1973) has had a strong impact in empirical asset pricing leading to numerous multifactor models. This paper shows that the explanatory power of the ICAPM application by Campbell and Vuolteenaho (2004) relies critically on the computation of Dimson (1979) covariances (betas). If one employs the standard factor covariances (excluding lagged factors), the two-factor ICAPM has virtually no explanatory power over the average returns of the 25 size/book-to-market portfolios. More specifically, it is the covariance with the lagged innovation in one of the state variables (the value spread) that drives the explanatory power of the model. These results are inconsistent with the central economic intuition from the ICAPM. By specifying a more general version of the ICAPM, the fit of the model improves relative to the Campbell and Vuolteenaho (2004) model.
23 citations
TL;DR: Li et al. as mentioned in this paper investigated the presence of a similar effect in the emerging Chinese stock markets with portfolio-level analysis and firm-level Fama-MacBeth cross-sectional regressions.
Abstract: Recent evidence in the U.S. and Europe indicates that stocks with high maximum daily returns in the previous month, perform poorly in the current month. We investigate the presence of a similar effect in the emerging Chinese stock markets with portfolio-level analysis and firm-level Fama-MacBeth cross-sectional regressions. We find evidence of a MAX effect similar to the U.S. and European markets though the effect appears stronger for longer holding periods. Contrary to U.S. and European evidence, the MAX effect in China does not weaken much less reverse the anomalous IV effect. Both the MAX and IV effects appear to independently coexist in the Chinese stock markets. Interpreted together with the strong evidence of risk-seeking behaviour among Chinese investors, our results are consistent with the suggestion that the negative MAX effect is driven by investor preference for stocks with lottery-like features.
23 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