Conditional Skewness in Asset Pricing Tests


Book Description

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model which incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of expected returns across assets and is significant even when factors based on size and book-to-market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent a year. Our results also suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios.




Time-Varying Conditional Skewness and the Market Risk Premium


Book Description

Single factor asset pricing models face two major hurdles: the problematic time-series properties of the ex ante market risk premium and the inability of the risk measure to account for a substantial degree of the cross-sectional variation of expected excess returns. We provide an explanation for the first failure using the following intuition: if investors know that the asset returns have conditional skewness given the information known today, the expected excess returns should include rewards for accepting skewness. We formalize this intuition with an asset pricing model which incorporates conditional skewness. We decompose the expected excess returns into components due to conditional variance and skewness. Our results show that conditional skewness is important and, when combined with the economy-wide reward for skewness, helps explain the time-variation of the ex ante market risk premiums. Conditional skewness has greater success in explaining the ex ante risk premium for the world portfolio than for the U.S. portfolio.




Conditional Coskewness and Asset Pricing


Book Description

We explore the empirical usefulness of conditional coskewness to explain the cross-section of equity returns. We find that coskewness is an important determinant of the returns to equity, and that the pricing relationship varies through time. In particular we find that when the conditional market skewness is positive investors are willing to sacrifice 7.87% annually per unit of gamma (a standardized measure of coskewness risk) while they only demand a premium of 1.80% when the market is negatively skewed. A similar picture emerges from the coskewness factor of Harvey and Siddique (1999) (a portfolio that is long stocks with small coskewness with the market and short high coskewness stocks) which earns 5.00% annually when the market is positively skewed but only 2.81% when the market is negatively skewed. The conditional two-moment CAPM and a conditional Fama and French (1993) three-factor model are rejected, but a model which includes coskewness is not rejected by the data. The model also passes a structural break test which many existing asset pricing models fail.




Multi-moment Asset Allocation and Pricing Models


Book Description

While mainstream financial theories and applications assume that asset returns are normally distributed and individual preferences are quadratic, the overwhelming empirical evidence shows otherwise. Indeed, most of the asset returns exhibit “fat-tails” distributions and investors exhibit asymmetric preferences. These empirical findings lead to the development of a new area of research dedicated to the introduction of higher order moments in portfolio theory and asset pricing models. Multi-moment asset pricing is a revolutionary new way of modeling time series in finance which allows various degrees of long-term memory to be generated. It allows risk and prices of risk to vary through time enabling the accurate valuation of long-lived assets. This book presents the state-of-the art in multi-moment asset allocation and pricing models and provides many new developments in a single volume, collecting in a unified framework theoretical results and applications previously scattered throughout the financial literature. The topics covered in this comprehensive volume include: four-moment individual risk preferences, mathematics of the multi-moment efficient frontier, coherent asymmetric risks measures, hedge funds asset allocation under higher moments, time-varying specifications of (co)moments and multi-moment asset pricing models with homogeneous and heterogeneous agents. Written by leading academics, Multi-moment Asset Allocation and Pricing Models offers a unique opportunity to explore the latest findings in this new field of research.




Variance Spillover and Skewness in Financial Asset Returns


Book Description

Bond and stock returns have been observed in the literature to exhibit unconditional skewness and temporal persistence in conditional skewness. We demonstrate that observed persistence in conditional third central moments can be due to the spillover of conditional variance dynamics. The confounding of true skewness and a variance spillover effect is problematic for financial modeling. Using market data, we empirically demonstrate that a simple standardization approach removes the variance-induced skewness persistence. An important implication is that more parsimonious return and asset pricing models result if skewness persistence need not be modeled.




Pricing Risk for Nonnormal Processes and Conditional Higher-order Moments


Book Description

This study introduces a new class of conditional asset pricing models that allows for higher order moments in pricing risk when the underlying returns generating process is nonnormal. The two main ingredients of the model are that nonnormality is captured by a generalized Student's t distribution and that time-varying conditional moments display GARCH-type structures. The framework is applied to the equal-weighted and value-weighted CRSP and NASDAQ indices using daily data over the period from January 2, 1990, to December 29, 1995. The main result of the empirical analysis is that both conditional skewness and conditional kurtosis, in addition to the conditional standard deviation, are priced in these portfolios. The empirical results also show that the exclusion of higher order moments results in misspecification errors. Furthermore, this study explores ways in which standard testing procedures can be employed to examine the validity of restrictions within the generalized exponential family. A Lagrange multiplier test for kurtosis, skewness, and normality was constructed. This test is more convenient than both the likelihood ratio and Wald tests, for it is easier to use with the constrained model.