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.     

Is idiosyncratic volatility priced in commodity futures markets?

This article investigates the relationship between expected returns and past idiosyncratic volatility in commodity futures markets. Measuring the idiosyncratic volatility of 27 commodity futures contracts with traditional pricing models that fail to account for backwardation and contango leads to the puzzling finding that idiosyncratic volatility is significantly negatively priced cross-sectionally. However, idiosyncratic volatility is not priced when the phases of backwardation and contango are suitably factored in the pricing model. A time-series portfolio analysis similarly suggests that failing to recognize the fundamental risk associated with the inexorable phases of backwardation and contango leads to overstated profitability of the idiosyncratic volatility mimicking portfolios.     

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.     

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.     

Additional evidence on transparency and bank financial performance

This paper examines the relationship between volatility and the probability of occurrence of expected extreme returns in the Canadian market. Four measures of volatility are examined: implied volatility from firm option prices, conditional volatility calculated using an EGARCH model, idiosyncratic volatility, and expected shortfall. A significantly positive relationship is observed between a firm's idiosyncratic volatility and the probability of occurrence of an extreme return in the subsequent month for firms. A 10% increase in idiosyncratic volatility in a given month is associated with the probability of an extreme shock in the subsequent month (top or bottom 1.5% of the returns distribution) of 26.4%. Other firm characteristics, including firm age, price, volume and book‐to‐market ratio, are also shown to be significantly related to subsequent firm extreme returns. The effects of conditional and implied volatility are mixed. The E‐GARCH and expected shortfall measures of conditional volatility are consistent with mean reversion: high short term realizations of conditional volatility foreshadow a lower probability of extreme returns.     

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.     

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.     

Idiosyncratic volatility and equity returns: UK evidence

The proposition that idiosyncratic volatility may matter in asset pricing is currently a topic of research and controversy. Using data from the UK market we examine the predictive ability of various measures of idiosyncratic risk and provide evidence which suggests that: (a) it is the idiosyncratic volatility of small capitalization stocks that matters for asset pricing and (b) that small stocks idiosyncratic volatility predicts the small capitalization premium component of market returns and is unrelated to either the market or the value premium. The predictive power of the aggregate idiosyncratic volatility of small stocks remains intact even after we control for the possible proxying effects of business cycle fluctuations and liquidity and is robust across time and different econometric specifications.     

The cross section of cashflow volatility and expected stock returns

I show that historical cashflow volatility is negatively related to future returns cross-sectionally. The negative association is large; economically meaningful; long-lasting up to five years; robust to known return-informative effects of size, value, price and earnings momentums and illiquidity; and extends to both systematic and idiosyncratic cashflow volatilities. Using the standard deviations of cashflow to sales and of cashflow to book equity as proxies for cashflow volatility, the least volatile decile portfolio outperforms the most volatile decile portfolio by 13% a year relative to the Fama–French four factors. The cashflow volatility effect is closely related to the idiosyncratic return volatility effect documented in Ang et al. [Ang, A., Hodrick, R.J., Xing, Y. and Zhang, X. “The cross-section of volatility and expected returns.” Journal of Finance, 51 (2006), 259–299.]. However, in portfolios simultaneously sorted on both cashflow and return volatilities, and in cross sectional regressions of returns at the firm level, these two effects neither drive out nor dominate each other. While the pricing of idiosyncratic cashflow volatility represents an anomaly against the traditional asset pricing theories, the pricing of historical cashflow uncertainty sheds light on potential fundamental risks embodied in the Fama–French HML and SMB factors.     

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...     

市场摩擦对特质风险溢价的影响效应——基于A股主板市场的实证分析

与国外发达市场相比,我国A股主板市场的市场摩擦因素对市场微观结构和资产定价的影响更大。在防范和化解系统性风险的过程中,进一步分析市场摩擦如何作用于特质风险定价效应的问题具有重要的理论和现实意义。本文通过采用多维市场摩擦指标来代理信息不对称、交易成本、买卖限制、卖空限制、风险对冲和外部冲击,检验中国股市特质风险和预期收益率的关系,并判断出市场摩擦因素间的差异性影响机制。回归发现,市场摩擦和特质风险因子(特质波动率和特质偏度)都具有定价效应。各维度市场摩擦因素降低了股票流动性,进而增强了特质波动率的负向定价效应,部分解释了“特质波动率之谜”,但市场摩擦对特质偏度因子溢价的影响较为微弱。同时,基于特质波动率和特质偏度因子的投资策略能够产生超越CAPM、三因子和五因子模型的绝对收益,并印证了市场摩擦对特质风险因子绝对收益的影响作用。     

Idiosyncratic risk does not matter: A re-examination of the relationship between average returns and average volatilities

A recent study by Goyal and Santa-Clara [J. Finance 58 (2003) 975] finds a significantly positive relationship between average stock returns and pre-determined average return volatility measures, while finding no relationship between the average return and its own volatility. The result is interpreted as evidence that idiosyncratic risk matters in asset pricing. We re-examine the issue in extended sample periods and find the proclaimed positive relationship is not substantiated. Our analysis indicates that the above-mentioned positive relationship is mainly driven by the data in the 1990s. The trading strategy suggested by Goyal and Santa-Clara to exploit the return predictability by pre-determined volatility does not yield sustained economic gains.     

Foreign exchange risk and the cross-section of stock returns

We examine the relation between the cross-section of US stock returns and foreign exchange rates during the period from 1973 to 2002. We find that stocks most sensitive to foreign exchange risk (in absolute value) have lower returns than others. This implies a non-linear, negative premium for foreign exchange risk. Sensitivity to foreign exchange generates a cross-sectional spread in stock returns unexplained by existing asset-pricing models. Consequently, we form a zero-investment factor related to foreign exchange-sensitivity and show that it can reduce mean pricing errors for exchange-sensitive portfolios. One possible explanation for our findings includes Johnson's [2004. Forecast dispersion and the cross-section of expected returns. Journal of Finance, 59, 1957–1978] option-theoretic model in which expected returns are decreasing in idiosyncratic cashflow volatility.     

Liquidity costs,idiosyncratic volatility and expected stock returns

This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to eliminate microstructure influences from stock closing price-based returns and then estimate IV. We show that there is a premium for IV in value-weighted portfolios, but this premium is less strong after correcting returns for microstructure bias. We further show that this premium is driven by liquidity in the prior month after correcting returns for microstructure noise. The pricing results from equally-weighted portfolios indicate that IV does not predict returns either before or after controlling for liquidity costs. These findings are robust after controlling for common risk factors as well as analysing double-sorted portfolios based on IV and liquidity.     

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.     

Media Coverage and the Cross-section of Stock Returns

By reaching a broad population of investors, mass media can alleviate informational frictions and affect security pricing even if it does not supply genuine news. We investigate this hypothesis by studying the cross-sectional relation between media coverage and expected stock returns. We find that stocks with no media coverage earn higher returns than stocks with high media coverage even after controlling for well-known risk factors. These results are more pronounced among small stocks and stocks with high individual ownership, low analyst following, and high idiosyncratic volatility. Our findings suggest that the breadth of information dissemination affects stock returns.     

Are extreme returns priced in the stock market? European evidence

This paper revisits some recently found evidence in the literature on the cross-section of stock returns for a carefully constructed dataset of euro area stocks. First, we confirm recent results for US data and find evidence of a negative cross-sectional relation between extreme positive returns and average returns after controlling for characteristics such as momentum, book-to-market, size, liquidity and short term return reversal. We argue that this is the case because these stocks have lottery-like characteristics, which is attractive to certain investors. Also, these stocks tend to be very volatile so that arbitrageurs are discouraged from correcting potential mispricing. As a consequence, these stocks are often overpriced and hence face lower expected returns. Second, when we control for extreme returns, the recently found negative relationship between idiosyncratic risk and future returns is less robust. In our models, after adding maximum returns, the relationship is insignificant and sometimes even positive. We also find that idiosyncratic skewness and coskewness play an important role for asset pricing, as predicted by several theoretical models.     

Have European Stocks become More Volatile? An Empirical Investigation of Idiosyncratic and Market Risk in the Euro Area

We examine the dynamics of idiosyncratic risk, market risk and return correlations in European equity markets using weekly observations from 3515 stocks listed in the 12 euro area stock markets over the period 1974–2004. Similarly to Campbell et al. (2001) , we find a rise in idiosyncratic volatility, implying that it now takes more stocks to diversify away idiosyncratic risk. Contrary to the US, however, market risk is trended upwards in Europe and correlations are not trended downwards. Both the volatility and correlation measures are pro‐cyclical, and they rise during times of low market returns. Market and average idiosyncratic volatility jointly predict market wide returns, and the latter impact upon both market and idiosyncratic volatility. This has asset pricing and risk management implications.     

Idiosyncratic Volatility and the Expected Stock Returns for Exploring the Relationship with Panel Threshold Regression

This paper utilizes panel threshold regression to study the impact of idiosyncratic risk of stock returns on the Taiwan Security Market over the period from 2000 to 2011, during which there has been a noticeable increase idiosyncratic volatility. An innovative panel threshold regression model is applied to test the panel threshold effect of idiosyncratic risk on expected stock returns. The results support Merton’s (J Financ 42:579–590, 1987) investor recognition hypothesis and confirm that a threshold effect does exist. This study shows that it is possible to identify the definitive level beyond which a further increase in idiosyncratic volatility does not improve proportional expected stock returns. Some important policy implications arise from these findings. The conditional distribution of expected stock returns is allowed to vary across low volatility states. The evidence suggests that in Taiwan, idiosyncratic risk is a predictor of future market returns based upon threshold value during the lower variance state. In contrast, when the threshold value is exceeded, the relation between idiosyncratic risk and expected stock returns is not statistically significant.