Pricing Kernels With Coskewness And Volatility Risk

I investigate a pricing kernel in which coskewness and the market volatility risk factors are endogenously determined. I show that the price of coskewness and market volatility risk are restricted by investor risk aversion and skewness preference. The risk aversion is estimated to be between two and five and significant. The price of volatility risk ranges from -1.5% to -0.15% per year. Consistent with theory, I find that the pricing kernel is decreasing in the aggregate wealth and increasing in the market volatility. When I project my estimated pricing kernel on a polynomial function of the market return, doing so produces the puzzling behaviors observed in pricing kernel. Using pricing kernels, I examine the sources of the idiosyncratic volatility premium. I find that nonzero risk aversion and firms' non-systematic coskewness determine the premium on idiosyncratic volatility risk. 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, different measures of market volatility and firm characteristics.

I derive pricing kernels in which the market volatility is endogenously determined. Using the Taylor expansion series of the representative investor's marginal utility, I show that the price of market volatility risk is restricted by the investor's risk aversion and skewness preference. The risk aversion is estimated to be between two and five and is significant. The price of the market volatility is negative. Consistent with economic theory, I find that the pricing kernel decreases in the market index return and increases in market volatility. The projection of the estimated pricing kernel onto a polynomial function of the market return produces puzzling behaviors, which can be observed in the pricing kernel and absolute risk aversion functions. The inclusion of additional terms in the Taylor expansion series of the investor's marginal utility produces a pricing kernel function of market stochastic volatility, stochastic skewness, and stochastic kurtosis. The prices of risk of these moments are restricted by the investor's risk aversion, skewness preference, and kurtosis preference. The prices of risk of these moments should not be confused with the price of risk of powers of the market return, such as co-skewness and co-kurtosis.

An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

By considering options on the S&P 500 it is shown how (i) missing option data, (ii) misestimated subjective probabilities, and (iii) a time varying variance (risk-premium) lead to different empirical pricing kernels. Accounting for all these aspects, the empirical return pricing kernel is w-shaped in calm periods and u-shaped in turbulent times. Finding w-shaped return pricing kernels, this paper is the first to reveal the interconnectedness between two asset pricing "puzzles": non-monotonically decreasing return pricing kernels and u-shaped volatility pricing kernels. Based on the non-parametric estimates, a parametric option pricing model that matches the stylized facts in the return and volatility dimension is proposed. Moreover, it is shown how a simple ambiguity aversion economy generates w- and u-shaped return pricing kernels.

We derive a lower bound for the size of the permanent component of asset pricing kernels. The bound is based on return properties of long-term zero-coupon bonds, risk-free bonds, and other risky securities. We find the permanent component of the pricing kernel to be very large; its volatility is about 100% of the volatility of the stochastic discount factor. This result implies that, if the pricing kernel is a function of consumption, innovations to consumption need to have permanent effects.

The dependence between the magnitudes of discontinuous changes in asset prices and contemporaneous discontinuous changes in volatility (co-jumps) is a fundamental aspect of the price process contributing, among other effects, to skewness in the return distribution. Yet, its nature has been reported by many as being - in terms of sign, magnitude, and statistical significance - largely elusive. Using a novel identification strategy for stochastic volatility modelling in continuous time relying on trade-level information for spot variance estimation, as well as infinitesimal cross-moments, this paper documents that a sizeable proportion of discontinuous changes in asset prices are associated with strongly anti-correlated, contemporaneous changes in volatility. Not only are the price jump sizes strongly negatively correlated with the volatility jump sizes, but the absolute values of their (negative) mean and dispersion appear to increase with the volatility level, an additional effect which should lead to care in the management of joint directional and volatility jump risk. Using a possibly non-monotonic pricing kernel, we illustrate the equilibrium impact of price and volatility co-jumps on both return and variance risk premia.

The coskewness-cokurtosis pricing model is equivalent to there not being any return for which the alpha is positive and for which the residual risk has positive coskewness and negative cokurtosis with the market. Such returns would be extremely attractive to investors with mean-variance-skewness-kurtosis preferences who hold the market portfolio. This result establishes a parallel to the CAPM, which is equivalent to the absence of positive alpha returns. It also establishes a parallel to the fundamental theorem of asset pricing, because it relates absence of portfolios with unusually good costs/payoffs to the existence of a stochastic discount factor of a particular type.