Cashflow risk,systematic earnings revisions,and the cross-section of stock returns

The returns of stocks are partially driven by changes in their expected cashflow. Using revisions in analyst earnings forecasts, we construct an analyst earnings beta that measures the covariance between the cashflow innovations of an asset and those of the market. A higher analyst earnings beta implies greater sensitivity to marketwide revisions in expected cashflow, and therefore higher systematic risk. Our analyst earnings beta captures exposure to macroeconomic fluctuations and has a positive risk premium that provides a partial explanation for the value premium, size premium, and long-term return reversals. From 1984 to 2005, 55.1% of the return variation across book-to-market, size, and long-term return reversal portfolios is captured by their analyst earnings betas.     

Size and earnings volatility of US bank holding companies

We examine whether bank earnings volatility depends on bank size. Using quarterly data for bank holding companies in the United States for the period 1995Q1–2010Q3 and controlling for the quality of management, leverage, and diversification, we find that bank size reduces return volatility. However, the effect is non-linear: when bank size exceeds a certain threshold (about US$5 billion) size is positively related to earnings volatility. The recent financial crisis decreased the threshold beyond which the impact of size on volatility turns positive.     

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.     

Dynamics and causality in industry-specific volatility

This paper presents comprehensive empirical evidence on the dynamics and causality within 30 US industry-specific volatilities during July 1963 and June 2008. We find that linear trends are present in 17 of the 30 industry volatilities. Granger-causality tests reveal that the industry of business supplies and the industry of finance are the most important lead indicators of industry volatilities. To uncover contemporaneous causal relationships in the market, we implement an emerging data-driven method of directed acyclic graphs. The results suggest that volatility shocks originating from business supplies, machinery, and consumer goods industries are sources of risks that affect most other industries. By contrast, volatilities in the two traditionally important industries, oil and autos, do not appear to have a substantial influence on other large industries in the contemporaneous time. Finally, business equipment and services, both containing information technology components, are the most important driving forces of the industry volatility surge in the late 1990s.     

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.     

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.     

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.     

Dispersion in analysts’ earnings forecasts and credit rating

This paper shows that the puzzling negative cross-sectional relation between dispersion in analysts’ earnings forecasts and future stock returns may be explained by financial distress, as proxied by credit rating downgrades. Focusing on a sample of firms rated by Standard & Poor's (S&P), we show that the profitability of dispersion-based trading strategies concentrates in a small number of the worst-rated firms and is significant only during periods of deteriorating credit conditions. In such periods, the negative dispersion–return relation emerges as low-rated firms experience substantial price drop along with considerable increase in forecast dispersion. Moreover, even for this small universe of worst-rated firms, the dispersion–return relation is non-existent when either the dispersion measure or return is adjusted by credit risk. The results are robust to previously proposed explanations for the dispersion effect such as short-sale constraints and leverage.     

Option-implied volatility factors and the cross-section of market risk premia

The main goal of this paper is to study the cross-sectional pricing of market volatility. The paper proposes that the market return, diffusion volatility, and jump volatility are fundamental factors that change the investors’ investment opportunity set. Based on estimates of diffusion and jump volatility factors using an enriched dataset including S&P 500 index returns, index options, and VIX, the paper finds negative market prices for volatility factors in the cross-section of stock returns. The findings are consistent with risk-based interpretations of value and size premia and indicate that the value effect is mainly related to the persistent diffusion volatility factor, whereas the size effect is associated with both the diffusion volatility factor and the jump volatility factor. The paper also finds that the use of market index data alone may yield counter-intuitive results.     

Maximum likelihood estimation of non-affine volatility processes

In this paper we develop a new estimation method for extracting non-affine latent stochastic volatility and risk premia from measures of model-free realized and risk-neutral integrated volatility. We estimate non-affine models with nonlinear drift and constant elasticity of variance and we compare them to the popular square-root stochastic volatility model. Our empirical findings are: (1) the square-root model is misspecified; (2) the inclusion of constant elasticity of variance and nonlinear drift captures stylized facts of volatility dynamics and (3) the square-root stochastic volatility model is explosive under the risk-neutral probability measure.     

Intraday volume and volatility relations with and without public news

This paper reexamines the dynamic relation between intraday trading volume and return volatility of large and small NYSE stocks in two partitioned samples, with and without identifiable public news. We argue that the sequential information arrival hypothesis (SIAH) can be tested only in periods containing public news. After partitioning the sample into periods with and without public news, we find bi-directional Granger-causality between volume and volatility in the presence of public information as hypothesized by the SIAH. Our analysis further suggests that return volatility is higher in the periods with public news, while trading volume is significantly higher in the no-news period; perhaps owing to the importance of private information for trading stocks. Using the sample without public news, we find evidence that volume Granger-causes volatility without feedback. These results are broadly consistent with behavioral models like the overconfidence and biased self-attribution model of [Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and over-reactions. Journal of Finance 53, 1839–1885]. It appears that overconfident investors overrate the precision of their private news signals and therefore trade too aggressively in the absence of public news; when public news arrives, investors’ biased self-attribution triggers excessive return volatility.     

Maximum likelihood estimation of stochastic volatility models

We develop and implement a method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by proxies based on the implied volatility of a short-dated at-the-money option. The approximation results in a small loss of accuracy relative to the standard errors due to sampling noise. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine Heston model and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.     

Financial market models with Lévy processes and time-varying volatility

Asset management and pricing models require the proper modeling of the return distribution of financial assets. While the return distribution used in the traditional theories of asset pricing and portfolio selection is the normal distribution, numerous studies that have investigated the empirical behavior of asset returns in financial markets throughout the world reject the hypothesis that asset return distributions are normally distribution. Alternative models for describing return distributions have been proposed since the 1960s, with the strongest empirical and theoretical support being provided for the family of stable distributions (with the normal distribution being a special case of this distribution). Since the turn of the century, specific forms of the stable distribution have been proposed and tested that better fit the observed behavior of historical return distributions. More specifically, subclasses of the tempered stable distribution have been proposed. In this paper, we propose one such subclass of the tempered stable distribution which we refer to as the “KR distribution”. We empirically test this distribution as well as two other recently proposed subclasses of the tempered stable distribution: the Carr–Geman–Madan–Yor (CGMY) distribution and the modified tempered stable (MTS) distribution. The advantage of the KR distribution over the other two distributions is that it has more flexible tail parameters. For these three subclasses of the tempered stable distribution, which are infinitely divisible and have exponential moments for some neighborhood of zero, we generate the exponential Lévy market models induced from them. We then construct a new GARCH model with the infinitely divisible distributed innovation and three subclasses of that GARCH model that incorporates three observed properties of asset returns: volatility clustering, fat tails, and skewness. We formulate the algorithm to find the risk-neutral return processes for those GARCH models using the “change of measure” for the tempered stable distributions. To compare the performance of those exponential Lévy models and the GARCH models, we report the results of the parameters estimated for the S&P 500 index and investigate the out-of-sample forecasting performance for those GARCH models for the S&P 500 option prices.     

Conditional volatility in affine term-structure models: Evidence from Treasury and swap markets

We study the ability of three-factor affine term-structure models to extract conditional volatility using interest rate swap yields for 1991–2005 and Treasury yields for 1970–2003. For the Treasury sample, the correlation between model-implied and EGARCH volatility is between 60% and 75%. For the swap sample, this correlation is rather low or negative. We find that these differences in model performance are primarily due to the timing of the swap sample, and not to institutional differences between swap and Treasury markets. We conclude that the ability of multifactor affine models to extract conditional volatility depends on the sample period, but that overall these models perform better than has been argued in the literature.     

The effect of mergers on implied volatility of equity options

This paper uses a sample of 25 large mergers from 1996 to 2004 to study the effect of mergers on the implied volatilities of equity options. The results indicate a statistically significant increase in volatility beyond the amount predicted if the transaction were effectively nothing more than a portfolio combination of the target and acquirer. The disparity suggests that, at least for the first 18 months after the transaction becomes effective, market participants expect mergers to increase risk. Integration risk and uncertainty about the extent to which efficiency gains and greater market power are realized are possible explanations for the discrepancy.     

Stock returns and volatility following the September 11 attacks: Evidence from 53 equity markets

September 11 attacks matter, and why not? Given that globalization has integrated financial markets, the magnitudes of the effect of the September 11 attacks on global markets are expected to be pervasive. We used data from 53 equity markets to investigate the short term impact of the September 11 attacks on markets' returns and volatility. Our empirical findings indicate that the impact of the attacks resulted in significant increases in volatility across regions and over the study period. However, stock returns experienced significant negative returns in the short-run but recovered quickly afterwards. Nevertheless, we find that the impact of the attacks on financial markets varied across regions. The implication here is that the less integrated regions (e.g., Middle East and North Africa) are with the international economy, the less exposed they are to shocks.     

The volatility of consumption-based stochastic discount factors and economic cycles

This paper aims to assess the macroeconomic and financial impact of economic uncertainty using information contained in the second moments of financial risk factors employed in the asset pricing literature. Specifically, we propose the volatility of consumption-based stochastic discount factors (SDFs) as a predictor of future economic and stock market cycles. We employ both contemporaneous and ultimate consumption risk specifications with durable and non-durable consumption. Alternative empirical tests show that this volatility has significant forecasting ability from 1985 to 2006. The degree of predictability tends to dominate that shown by standard predictor variables. We argue that the significant predictability of the volatility of consumption-based SDFs reported in this paper relies mainly on the joint effect of their components.     

Identifying volatility risk premia from fixed income Asian options

Fixed income options are frequently adopted by companies to hedge interest rate risk. Their payoff dependence on the cumulative short-term rate makes them particularly informative about interest rate volatility risk. Based on a joint dataset of bonds and Asian interest rate options, we study the interrelations between bond and volatility risk premia in a major emerging fixed income market. We propose a dynamic term structure model that generates an incomplete market compatible with a preliminary empirical analysis of the dataset. Approximation formulas for at-the-money Asian option prices avoid the use of computationally intensive Fourier transform methods, allowing for an efficient implementation of the model. The model generates a bond risk premium strongly correlated with a widely accepted emerging market benchmark index (EMBI-Global), and a negative volatility risk premium, consistent with the use of Asian options as insurance in this market.     

News-driven return reversals: Liquidity provision ahead of earnings announcements

This study documents a six-fold increase in short-term return reversals during earnings announcements relative to non-announcement periods. Following prior research, we use reversals as a proxy for expected returns market makers demand for providing liquidity. Our findings highlight significant time-series variation in the magnitude of short-term return reversals and suggest that market makers demand higher expected returns prior to earnings announcements because of increased inventory risks that stem from holding net positions through the release of anticipated earnings news. Collectively, our findings suggest that uncertainty regarding anticipated information events elicits predictable increases in the compensation demanded for providing liquidity and that these increases significantly affect the dynamics and information content of market prices.     

The cross-sectional relation between conditional heteroskedasticity,the implied volatility smile,and the variance risk premium

This paper estimates how the shape of the implied volatility smile and the size of the variance risk premium relate to parameters of GARCH-type time-series models measuring how conditional volatility responds to return shocks. Markets in which return shocks lead to large increases in conditional volatility tend to have larger variance risk premia than markets in which the impact on conditional volatility is slight. Markets in which negative (positive) return shocks lead to larger increases in future volatility than positive (negative) return shocks tend to have downward (upward) sloping implied volatility smiles. Also, differences in how volatility responds to return shocks as measured by GARCH-type models explain much, but not all, of the variations in excess kurtosis and multi-period skewness across different markets.