Trend in aggregate idiosyncratic volatility

We suggest that price interaction among stocks is an important determinant of idiosyncratic volatility. We demonstrate that as more (less) stocks are listed in the markets, price interaction among stocks increases (decreases), and hence stocks, on average, become more (less) volatile. Our results show that price interaction has a significant positive effect of idiosyncratic volatility. The results of various robustness checks indicate that the effect of price interaction is still significant to the presence of liquidity, newly listed firms, cash flow variables, business cycle variables, and market volatility. Once the price interaction effect is taken into account, no trend remains in idiosyncratic volatility. We conclude that there is no trend, but a reflection of the positive effect of price interaction on idiosyncratic volatility.     

The relationship between risk and expected return in Europe

We employ MIDAS (mixed data sampling) to study the risk–expected return trade-off in several European stock indices. Using MIDAS, we report that in most indices there is a significant positive relationship between risk and expected return. This strongly contrasts with the result we obtain when we employ both symmetric and asymmetric GARCH models for conditional variance. We also find that asymmetric specifications of the variance process within the MIDAS framework improve the relationship between risk and expected return. As an additional application, we analyze the extent to which European stock markets are integrated, which is a particularly relevant issue, especially following the launch of the Euro in January 1999. Finally, we propose a bivariate MIDAS specification to test the pricing significance of the hedging component within an intertemporal risk–return trade-off with multiple European market indices.     

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.     

Firm age and realized idiosyncratic return volatility in China: The role of short-sales constraints

This paper investigates how realized idiosyncratic return volatility changes with firm age in the Chinese stock market. By employing a sample of 26,676 firm-year observations of 2798 A-share listed Chinese firms from 2001 to 2019, we find that realized idiosyncratic return volatility is negatively associated with firm age. Further, we find that loosening short-sales constraints strengthens this negative association, and that heterogeneity of investor beliefs is the most likely mechanism driving the negative relation, rather than the alternative explanations of cash flow volatility and growth options. Our results are fairly consistent under two different measures of firm age, and are robust to a choice of two multiple-factor models (the Fama-French three-factor and five-factor models) as well as two data frequencies (daily and monthly) used to estimate realized idiosyncratic return volatility.     

Testing for negative expected market return premia

This paper tests the hypothesis that the expected return premium on the market portfolio is always non-negative. A violation of this lower bound restriction provides evidence against a broad class of risk-based equilibrium models in favor of bubble behavior. Our tests utilize information variables, identified in prior literature, that predict time variation in market return premia. We employ out-of-sample forecasts and bootstraps generated with parameters that are consistent with non-negativity but closest to the estimated parameters. We find statistically reliable evidence against non-negativity for the excess return on the value-weighted market index. The most negative out-of-sample prediction was −2.01% in September 1973.     

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.     

Incentive‐compatible contracts in merger negotiations: The role of acquirer idiosyncratic stock return volatility

We show that the acquiring firm's idiosyncratic stock return volatility (sigma) is an important determinant of the selection and perceived valuation effects of earnouts in Mergers and Acquisitions (M&As). Earnout‐based M&As are more often announced by high‐sigma acquirers (nearly 40% of all earnout‐based M&As), yet the documented higher risk‐adjusted returns accrued to acquirers in earnout‐based M&As, relative to M&As settled in cash, stock or mixed payments (the earnout effect), appear in deals announced by low‐sigma acquirers (nearly 20% of all earnout‐based M&As). High‐sigma acquirers employing earnouts appear to break even, or even experience losses, relative to their counterparts employing single up‐front payments. These results are confirmed based on a quasi‐experimental design through which the earnout effect is measured in isolation. We argue that in M&As announced by high‐sigma acquirers, the earnout effect is potentially elusive due to the presence of an acquirer‐specific information revelation effect, resulting from the heightened extent of information asymmetry between (small) acquirers’ managers and outside investors. On the contrary, the use of earnouts in M&As announced by low‐sigma (large) acquirers, whereby the acquirer‐specific information revelation effect is likely negligible, sends a strong signal for value creation that also prevents investors from inducing a size‐related discount.     

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.     

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.     

Estimation of expected return: CAPM vs. Fama and French

Most practitioners favour a one-factor model (CAPM) when estimating expected return for an individual stock. For estimation of portfolio returns, academics recommend the Fama and French three-factor model. The main objective of this paper is to compare the performance of these two models for individual stocks. First, estimates for individual stock returns based on CAPM are obtained using different time frames, data frequencies, and indexes. It is found that 5 years of monthly data and an equal-weighted index, as opposed to the commonly recommended value-weighted index, provide the best estimate. However, performance of the model is very poor; it explains on average 3% of differences in returns. Then, estimates for individual stock returns are obtained based on the Fama and French model using 5 years of monthly data. This model, however, does not do much better; independent of the index used, it explains on average 5% of differences in returns. These results therefore bring into question the use of either model for estimation of individual expected stock returns.     

The impact of risk and uncertainty on expected returns

We study asset pricing in economies featuring both risk and uncertainty. In our empirical analysis, we measure risk via return volatility and uncertainty via the degree of disagreement of professional forecasters, attributing different weights to each forecaster. We empirically model the typical risk-return trade-off and augment these models with our measure of uncertainty. We find stronger empirical evidence for an uncertainty-return trade-off than for the traditional risk-return trade-off. Finally, we investigate the performance of a two-factor model with risk and uncertainty in the cross section.     

Financial reporting quality and idiosyncratic return volatility

Campbell et al. (2001) document that firms’ stock returns have become more volatile in the U.S. since 1960. We hypothesize and find that deteriorating earnings quality is associated with higher idiosyncratic return volatility over 1962–2001. These results are robust to controlling for (i) inter-temporal changes in the disclosure of value-relevant information, sophistication of investors and the possibility that earnings quality can be informative about future cash flows; (ii) stock return performance, cash flow operating performance, cash flow variability, growth, leverage and firm size; and (iii) new listings, high-technology firms, firm-years with losses, mergers and acquisitions and financial distress.     

High idiosyncratic volatility and low returns: International and further U.S. evidence

Stocks with recent past high idiosyncratic volatility have low future average returns around the world. Across 23 developed markets, the difference in average returns between the extreme quintile portfolios sorted on idiosyncratic volatility is -1.31%$-1.31\%$ per month, after controlling for world market, size, and value factors. The effect is individually significant in each G7 country. In the United States, we rule out explanations based on trading frictions, information dissemination, and higher moments. There is strong covariation in the low returns to high-idiosyncratic-volatility stocks across countries, suggesting that broad, not easily diversifiable factors lie behind this phenomenon.     

The cross-section of dynamics in idiosyncratic risk

This paper investigates change in idiosyncratic volatility estimated by individual security. We find that a significant portion of securities contains long periods of increasing or decreasing idiosyncratic risk. The series of idiosyncratic risk though are unlikely to have a life-long deterministic trend, and can be often characterized by a long memory process. Our evidence suggests that the proportions of securities with rising and declining risk continuously change, which in turn affects fluctuations of the market average. This evidence implies that an average idiosyncratic risk may not be a good representative of the dynamics in risk of a given security. We demonstrate that the companies with an increasing idiosyncratic risk tend to have deteriorating performance, and investigate factors behind these empirical observations.     

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.     

The expected value premium

Fama and French [2002. The equity premium. Journal of Finance 57, 637–659] estimate the equity premium using dividend growth rates to measure expected rates of capital gain. We apply their method to study the value premium. From 1945 to 2005, the expected value premium is on average 6.1% per annum, consisting of an expected dividend growth component of 4.4% and an expected dividend price ratio component of 1.7%. Unlike the equity premium, the value premium has been largely stable over the last half century.     

The world price of jump and volatility risk

We study international integration of markets for jump and volatility risk, using index option data for the main global markets. To explain the cross-section of expected option returns we focus on return-based multi-factor models. For each market separately, we provide evidence that volatility and jump risk are priced risk factors. There is little evidence, however, of global unconditional pricing of these risks. We show that UK and US option markets have become increasingly interrelated, and using conditional pricing models generates some evidence of international pricing. Finally, the benefits of diversifying jump and volatility risk internationally are substantial, but declining.     

Forecasting foreign exchange rates using idiosyncratic volatility

Average idiosyncratic stock volatility forecasts the bilateral exchange rates of the US dollar against major foreign currencies in and out of sample. The US dollar tends to appreciate after an increase in US idiosyncratic volatility. Similarly, ceteris paribus, German and Japanese idiosyncratic volatilities positively and significantly correlate with future US dollar prices of the Deutsche mark and the Japanese yen, respectively. Our results suggest that exchange rates are predictable.