Title: An information-theoretic asset pricing model
Authors: Christian Julliard - London School of Economics (United Kingdom) [presenting]
Abstract: A non-parametric estimate of the pricing kernel, extracted using an information-theoretic approach, is shown to deliver smaller out-of-sample pricing errors and a better cross-sectional fit than leading factor models. The information SDF (I-SDF) identifies sources of risk not captured by standard factors, generating very large annual alphas (10\%-18\%) and Sharpe ratios (0.90-1.3). I-SDFs extracted from a wide cross-section of equity portfolios are highly positively skewed and leptokurtic, and imply that about half of the observed risk premia represent a compensation for tail risk. The I-SDF offers a powerful benchmark relative to which competing theories and investment strategies can be evaluated.