Downside risk and the size of credit spreads

We investigate why spreads on corporate bonds are so much larger than expected losses from default. Systematic factors make very little contribution to spreads, even if higher moments or downside effects are taken into account. Instead we find that sizes of spreads are strongly related to idiosyncratic-risk factors: not only to idiosyncratic equity volatility, but even more to idiosyncratic bond volatility and idiosyncratic bond value-at-risk. Idiosyncratic bond volatility helps to explain spreads because it reflects not just the distribution of firm value but is also a proxy for liquidity risk. Idiosyncratic bond value-at-risk adds to this by capturing the left-skewness of the firm-value distribution. We confirm our results both for the initial 1997-2004 sample period and also out of sample for 2005-2009, which includes the sub-prime crisis. Overall, credit spreads are large because they incorporate a large risk premium related to investors’ fears of extreme losses.     

Asymmetric Volatility,Skewness, and Downside Risk in Different Asset Classes: Evidence from Futures Markets

This study examines the cross‐sectional variation of futures returns from different asset classes. The monthly returns are positively correlated with downside risk and negatively correlated with coskewness. The asymmetric volatility effect generates negatively skewed returns. Assets with high coskewness and low downside betas provide hedges against market downside risk and offer low returns. The high returns offered by assets with low coskewness and high downside betas are a risk premium for bearing downside risk. The asset pricing model that incorporates downside risk partially explains the futures returns. The results indicate a unified risk perspective to jointly price different asset classes.     

Extreme downside risk and expected stock returns

We propose a measure for extreme downside risk (EDR) to investigate whether bearing such a risk is rewarded by higher expected stock returns. By constructing an EDR proxy with the left tail index in the classical generalized extreme value distribution, we document a significantly positive EDR premium in cross-section of stock returns even after controlling for market, size, value, momentum, and liquidity effects. The EDR premium is more prominent among glamor stocks and when high market returns are expected. High-EDR stocks are generally characterized by high idiosyncratic risk, large downside beta, lower coskewness and cokurtosis, and high bankruptcy risk. The EDR premium persists after these characteristics are controlled for. Although Value at Risk (VaR) plays a significant role in explaining the EDR premium, it cannot completely subsume the EDR effect.     

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.     

Investment,consumption, and hedging under incomplete markets

Entrepreneurs often face undiversifiable idiosyncratic risks from their business investments. We extend the standard real options approach to an incomplete markets environment and analyze the joint decisions of business investments, consumption/savings, and portfolio selection. For a lump-sum investment payoff and an agent with a sufficiently strong precautionary savings motive, an increase in volatility can accelerate investment, contrary to the standard real options analysis. When the agent can trade the market portfolio to partially hedge against investment risk, the systematic volatility is compensated via the standard CAPM argument, and the idiosyncratic volatility generates a private equity premium. Finally, when the investment payoff is a series of flows, the agent's idiosyncratic risk exposure alters both the implied option value and the implied project value, causing a reversal of the results in the lump-sum payoff case.     

Convergent synergies in the global market for corporate control

We investigate the informational role of the takeover premium as a forward looking price to expected synergies in the global market for corporate control. We find that premiums paid in the global market for corporate control are clustered in waves and driven to some extent by the US premium. International takeover premiums have become more responsive to US premiums as the globalization process evolved over time. Short-run divergent dynamics due to idiosyncratic or country-specific factors have become less severe, which suggests that expected synergies have become increasingly integrated in the global market for corporate control. Furthermore, we find that the region’s takeover premiums typically become more responsive to US takeover premiums when US economic conditions are relatively weak, when the US monetary policy is restrictive, when US credit risk is high, and when the region’s corporate governance (as measured by legal system quality and accounting quality) is high.     

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.     

Credit default swap spreads and variance risk premia

We find that the firm-level variance risk premium has a prominent explanatory power for credit spreads in the presence of market- and firm-level control variables established in the existing literature. Such predictability complements that of the leading state variable—the leverage ratio—and strengthens significantly with a lower firm credit rating, longer credit contract maturity, and model-free implied variance. We provide further evidence that (1) the variance risk premium has a cleaner systematic component than implied variance or expected variance, (2) the cross-section of firms’ variance risk premia capture systematic variance risk in a stronger way than firms’ equity returns in capturing market return risk, and (3) a structural model with stochastic volatility can reproduce the predictability pattern of variance risk premia for credit spreads.     

The second moment matters! Cross-sectional dispersion of firm valuations and expected returns

Behavioral theories predict that firm valuation dispersion in the cross-section (“dispersion”) measures aggregate overpricing caused by investor overconfidence and should be negatively related to expected aggregate returns. This paper develops and tests these hypotheses. Consistent with the model predictions, I find that measures of dispersion are positively related to aggregate valuations, trading volume, idiosyncratic volatility, past market returns, and current and future investor sentiment indexes. Dispersion is a strong negative predictor of subsequent short- and long-term market excess returns. Market beta is positively related to stock returns when the beginning-of-period dispersion is low and this relationship reverses when initial dispersion is high. A simple forecast model based on dispersion significantly outperforms a naive model based on historical equity premium in out-of-sample tests and the predictability is stronger in economic downturns.     

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 Covariance and Expected Stock Returns

Given that the idiosyncratic volatility (IDVOL) of individual stocks co‐varies, we develop a model to determine how aggregate idiosyncratic volatility (AIV) may affect the volatility of a portfolio with a finite number of stocks. In portfolio and cross‐sectional tests, we find that stocks whose returns are more correlated with AIV innovations have lower returns than those that are less correlated with AIV innovations. These results are robust to controlling for the stock's own IDVOL and market volatility. We conclude that risk‐averse investors pay a premium for stocks that pay well when AIV is high, consistent with our model.     

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.     

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.     

Arbitrage risk and the turnover anomaly

A strong turnover premium exists such that stocks with lower turnover have higher future returns in the 5 years following their formation than those with higher turnover. This turnover premium cannot be explained by existing asset-pricing models, a risk-based liquidity factor, or anomalies such as size, book-to-market ratio, or momentum. Further analysis indicates that the turnover premium is greater for stocks with higher idiosyncratic volatility, higher transaction costs, lower institutional ownership, and lower investor sophistication, which implies it is consistent with the mispricing explanation based on arbitrage risk.     

Impact of idiosyncratic volatility on stock returns: A cross-sectional study

This paper proposes a new approach to estimate the idiosyncratic volatility premium. In contrast to the popular two-pass regression method, this approach relies on a novel GMM-type estimation procedure that uses only a single cross-section of return observations to obtain consistent estimates. Also, it enables a comparison of idiosyncratic volatility premia estimated using stock returns with different holding periods. The approach is empirically illustrated by applying it to daily, weekly, monthly, quarterly, and annual US stock return data over the course of 2000–2011. The results suggest that the idiosyncratic volatility premium tends to be positive on daily return data, but negative on monthly, quarterly, and annual data. They also indicate the presence of a January effect.     

Do return prediction models add economic value?

We compare statistical and economic measures of forecasting performance across a large set of stock return prediction models with time-varying mean and volatility. We find that it is very common for models to produce higher out-of-sample mean squared forecast errors than a model assuming a constant equity premium, yet simultaneously add economic value when their forecasts are used to guide portfolio decisions. While there is generally a positive correlation between a return prediction model’s out-of-sample statistical performance and its ability to add economic value, the relation tends to be weak and only explains a small part of the cross-sectional variation in different models’ economic value.     

Idiosyncratic Risk,Investor Base,and Returns

Using four different proxies for a firm's investor base we demonstrate that idiosyncratic risk premiums are larger for neglected stocks and smaller or economically insignificant for visible stocks. Since neglected stocks have greater idiosyncratic volatility (IV), the total IV risk premium (price × quantity) for neglected stocks will be greater than that of visible stocks. Additionally, we find a positive size effect and negative beta effect after controlling for IV. Overall, our results provide strong support for Merton's theory that market segmentation induced by incomplete information is an important component of the influence of IV in the cross‐section of returns.     

Asymmetric stock market volatility and the cyclical behavior of expected returns

Recent explanations of aggregate stock market fluctuations suggest that countercyclical stock market volatility is consistent with rational asset evaluations. In this paper, I develop a framework to study the causes of countercyclical stock market volatility. I find that countercyclical risk premia do not imply countercyclical return volatility. Instead, countercyclical stock volatility occurs if risk premia increase more in bad times than they decrease in good times, thereby inducing price–dividend ratios to fluctuate more in bad times than in good. The business cycle asymmetry in the investors’ attitude toward discounting future cash flows plays a novel and critical role in many rational explanations of asset price fluctuations.     

Idiosyncratic volatility,fundamentals, and institutional herding: Evidence from the Japanese stock market

Using Japanese data from 1975 to 2003, we show that both institutional herding and firm earnings are positively related to idiosyncratic volatility. We reject the hypothesis that institutional investors herd toward stocks with high idiosyncratic volatility and systematic risk. Our results suggest that a behavior story may explain the negative premium earned by high idiosyncratic volatility stocks found by Ang et al. [Ang, Andrew, Hodrick, Robert J., Yuhang Xing, Xiaoyan Zhang, 2004. The cross-section of volatility and expected returns, Forthcoming Journal of Finance]. We also find that the dispersions of change in institutional ownership and return-on-asset move together with the market aggregate idiosyncratic volatility over time. Our results suggest that investor behavior and stock fundamentals may both help explain the time-series pattern of market aggregate idiosyncratic volatility.