The return impact of realized and expected idiosyncratic volatility

We show that the negative relation between realized idiosyncratic volatility, measured over the prior month, and returns is robust in non-January months. Controlling for realized idiosyncratic volatility, we show that the relation between returns and expected idiosyncratic volatility is positive and robust. Realized and expected idiosyncratic volatility are separate and important effects describing the cross-section of returns. We find the negative return on a zero-investment portfolio that is long high realized idiosyncratic volatility stocks and short low realized idiosyncratic volatility stocks is dependent on aggregate investor sentiment. In cross-sectional tests, we find the negative relation is weaker for stocks with a large analyst following and stronger for stocks with high dispersion of analyst forecasts. The positive relation between expected idiosyncratic volatility and returns is not due to mispricing.     

EXPECTED VOLATILITY,UNEXPECTED VOLATILITY,AND THE CROSS‐SECTION OF STOCK RETURNS

The existing literature finds conflicting results on the cross‐sectional relation between expected returns and idiosyncratic volatility. We contend that at the firm level, the sample correlation between unexpected returns and expected idiosyncratic volatility can cloud the true relation between the expected return and expected idiosyncratic volatility. We show strong evidence that unexpected idiosyncratic volatility is positively related to unexpected returns. Using unexpected idiosyncratic volatility to control for unexpected returns, we find expected idiosyncratic volatility to be significantly and positively related to expected returns. This result holds after controlling for various firm characteristics, and it is robust across different sample periods.     

Idiosyncratic Volatility and Firm‐Specific News: Beyond Limited Arbitrage

We examine the relation between idiosyncratic volatility and returns around news announcements. Mispricing is most likely to occur during news announcements. If idiosyncratic volatility generates a limit to arbitrage, then the negative relation between returns and news volatility should be stronger than the relation to nonnews volatility. Instead, we find nonnews volatility has a robust negative relation to returns and lacks key features expected if volatility were a reflection of limits to arbitrage. Pricing of nonnews volatility is related to lottery‐like features of a stock's return. Our results suggest that volatility has a price effect beyond a limit to arbitrage.     

Volatilities and Momentum Returns in Real Estate Investment Trusts

We examine the relation of time-varying idiosyncratic risk and momentum returns in REITs using a GARCH-in-mean model and incorporate liquidity risk in the asset pricing model. This is important because illiquidity may be more severe for REITs due to the nature of their underlying assets. We find that momentum returns display asymmetric volatility, i.e., momentum returns are higher when volatility is higher. Additionally, we find evidence that REITs with lowest past returns (losers) have higher idiosyncratic risks than those with highest past returns (winners) and that investors require a lower risk premium for holding losers’ idiosyncratic risks. Therefore, although losers have higher levels of idiosyncratic risks, their low risk premia cause low returns, which contribute to momentum. Lastly, we find a positive relation between REITs’ momentum return and turnover.     

Idiosyncratic Cash Flows and Systematic Risk

We show that unpriced cash flow shocks contain information about future priced risk. A positive idiosyncratic shock decreases the sensitivity of firm value to priced risk factors and simultaneously increases firm size and idiosyncratic risk. A simple model can therefore explain book‐to‐market and size anomalies, as well as the negative relation between idiosyncratic volatility and stock returns. Empirically, we find that anomalies are more pronounced for firms with high idiosyncratic cash flow volatility. More generally, our results imply that any economic variable correlated with the history of idiosyncratic shocks can help to explain expected stock returns.     

Is idiosyncratic volatility priced in cryptocurrency markets?

This paper investigates how idiosyncratic volatility is priced in the cross-section of cryptocurrency returns. By conducting both portfolio-level analysis and Fama-MacBeth regression analysis, we demonstrate that idiosyncratic volatility is positively related to the expected returns of cryptocurrencies. This finding is not subsumed by effects of size, momentum, liquidity, volume, and price and is robust to different weighting schemes, holding periods, and sample sizes. Besides, we find no evidence of temporal relation between idiosyncratic volatility and returns in cryptocurrency markets.     

Corporate Bond Returns and Volatility

Recent literature emphasizes the relation of stock volatility to corporate bond yields. We demonstrate that during 1996–2005 corporate bond excess return volatility is directly related to contemporaneous corporate bond excess returns. In fact, the decompositions of aggregate bond volatility have a higher contemporaneous correlation with bond yields in comparison to idiosyncratic stock risk. Additionally, bond volatility and idiosyncratic risk are significant predictors of corporate three‐month and six‐month ahead bond excess returns. We also find that corporate bond volatility contains both slow moving and time‐varying components.     

Expected Idiosyncratic Volatility Measures and Expected Returns

We find that idiosyncratic volatility forecasts using information available to traders at the time of the forecast are not related to expected returns. The positive relation documented in a number of other papers only exists when forward‐looking information is incorporated into the volatility estimate. That positive relation is driven by the realized idiosyncratic volatility component that cannot be forecasted by investors. Our findings are robust to several different empirical tests, volatility forecasting models and time periods.     

Idiosyncratic risk and expected returns in frontier markets: Evidence from GCC

We investigate the pricing of idiosyncratic volatility of seven frontier markets in six GCC countries. We find a significant (marginal) negative relationship between expected returns and lagged idiosyncratic volatility for individual stocks in Saudi Arabia (Qatar) but none in Kuwait and Abu Dhabi. However, when we estimate conditional idiosyncratic volatility either by EGARCH or AR Models, the relationship turns positive. Introducing unexpected idiosyncratic volatility as an explanatory variable to control for any unexpected returns uncovers the true relationship between expected idiosyncratic volatility and expected returns. The evidence turns out to be robust for return reversals and other control variables. Moreover, the pricing of idiosyncratic risk is less evident in higher country governance and seems to be unrelated to the degree of financial development.     

Explaining the idiosyncratic volatility puzzle using Stochastic Discount Factors

I use Stochastic Discount Factors to examine the sources of the idiosyncratic volatility premium. I find that non-zero risk aversion and firms’ non-systematic coskewness determine the premium on idiosyncratic volatility risk. The firm’s non-systematic coskewness measures the comovement of the asset’s volatility with the market return. When I control for the non-systematic coskewness factor, I find no significant relation between idiosyncratic volatility and stock expected returns. My results are robust across different sample periods and firm characteristics.     

Idiosyncratic volatility and cross‐sectional stock returns in Southeast Asian stock markets

We examine the role of idiosyncratic risk in five ASEAN markets of Malaysia, Singapore, Thailand, Indonesia, and the Philippines. Our research was motivated by the findings of Ang et al. (2006, 2009) of a ‘puzzling’ negative relation between idiosyncratic volatility and 1‐month ahead stock returns in developed markets and the suggestion of the ubiquity of these results in other markets. In contrast, we find no evidence of an idiosyncratic volatility puzzle in these Asian stock markets; instead, we document a positive relationship between idiosyncratic volatility and returns in Malaysia, Singapore, Thailand, and Indonesia and no relationship in the Philippines. The idiosyncratic volatility trading strategy could result in significant trading profits in Malaysia, Singapore, Thailand, and to some extent in Indonesia. Our study underscores the fact that generalizing empirical results obtained in developed stock markets to new and emerging markets could potentially be misleading.     

Idiosyncratic volatility puzzle: influence of macro-finance factors

Review of Quantitative Finance and Accounting - We analyze the cross-sectional relation between expected idiosyncratic volatility and stock returns. The expected idiosyncratic volatility is...     

Idiosyncratic risk and the cross-section of expected stock returns

Theories such as Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] predict a positive relation between idiosyncratic risk and expected return when investors do not diversify their portfolio. Ang, Hodrick, Xing, and Zhang [2006. The cross-section of volatility and expected returns. Journal of Finance 61, 259–299], however, find that monthly stock returns are negatively related to the one-month lagged idiosyncratic volatilities. I show that idiosyncratic volatilities are time-varying and thus, their findings should not be used to imply the relation between idiosyncratic risk and expected return. Using the exponential GARCH models to estimate expected idiosyncratic volatilities, I find a significantly positive relation between the estimated conditional idiosyncratic volatilities and expected returns. Further evidence suggests that Ang et al.'s findings are largely explained by the return reversal of a subset of small stocks with high idiosyncratic volatilities.     

The MAX effect: European evidence

The maximum daily return over the previous month (MAX) of Bali et al. (2011) is a strong and significant predictor of future stock returns in non-U.S. equity markets. Once it is controlled for MAX in the cross-section of average returns, the puzzling negative idiosyncratic volatility-return relation disappears. Consistent with the assumption that MAX is the true effect, for which idiosyncratic volatility is just a proxy, we find that MAX can be traced back to firm fundamentals in the manner of idiosyncratic volatility. The negative MAX-return relation is stronger among firms with high cash flow volatility and weaker among firms with high profitability.     

Stock market volatility: friend or foe?

Although a good deal of research effort has been allocated to understanding the time-series volatility of stock returns – as both market (or systematic) volatility and idiosyncratic (or non-systematic) volatility – the relationship of such volatility with cross-sectional volatility or dispersion of outcomes is sparse. Nevertheless, the quest to understand one must involve the quest to understand the other. In this paper, we investigate the dispersion of returns in relation to inter-temporal volatility, as well as the dynamic of dispersion of returns in generating a portfolio’s return outcome. We find that the level of such dispersion is highly significant for portfolio performance and the notion of risk.     

Incomplete information,idiosyncratic volatility and stock returns

When investors have incomplete information, expected returns, as measured by an econometrician, deviate from those predicted by standard asset pricing models by including a term that is the product of the stock’s idiosyncratic volatility and the investors’ aggregated forecast errors. If investors are biased this term generates a relation between idiosyncratic volatility and expected stocks returns. Relying on forecast revisions from IBES, we construct a new variable that proxies for this term and show that it explains a significant part of the empirical relation between idiosyncratic volatility and stock returns.     

Fundamentals or Managerial Discretion? The Relationship between Accrual Variability and Future Stock Return Volatility

This study extends the theoretical framework of Callen and Segal (2004) and Vuolteenaho (2002) to investigate the association between accrual variability and firm‐level stock return volatility. The empirical evidence supports our prediction that increased uncertainty in current‐period accounting accruals is associated with significantly higher volatility of future stock returns, and the results are valid for measures of both systematic and idiosyncratic volatility. When accrual variability is decomposed into fundamental and discretionary portions, we find that the positive relationship between accrual variability and future stock return volatility is dominated by the fundamental component of accrual variability. Overall, our results suggest that uncertainty reflected in accrual information is subsequently reflected in the fluctuation of future stock returns, and that the predictive content in accruals primarily reflects firms' fundamental uncertainty, rather than any effects of managerial choices and interventions in the accounting process.     

Price Momentum and Idiosyncratic Volatility

We find that returns to momentum investing are higher among high idiosyncratic volatility ( IVol) stocks, especially high IVol losers. Higher IVol stocks also experience quicker and larger reversals. The findings are consistent with momentum profits being attributable to underreaction to firm‐specific information and with IVol limiting arbitrage of the momentum effect. We also find a positive time‐series relation between momentum returns and aggregate IVol. Given the long‐term rise in IVol, this result helps explain the persistence of momentum profits since Jegadeesh and Titman's (1993) study.     

Daily short interest,idiosyncratic risk,and stock returns

This paper examines the relation between short selling and returns and the impact of arbitrage costs on short sellers’ behavior. Using daily UK short selling data, we find that stocks with low short interest levels experience significant positive returns on both an equal- and value-weighted basis. Economic theory predicts that short sellers avoid establishing positions in stocks with high idiosyncratic risk. Our results indicate a negative relation between short interest and returns among high idiosyncratic risk stocks and that short selling activity is mostly concentrated in low idiosyncratic risk stocks where it is less costly to arbitrage fundamental risk.     

The Cross-Section of Volatility and Expected Returns

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. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) 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.