An Information-Theoretic Asset Pricing Model

被引：0

作者：

Ghosh, Anisha ^{[1
]}

Julliard, Christian ^{[2
,3
]}

Taylor, Alex P. ^{[4
]}

机构：

[1] McGill Univ, Desautels Fac Management, Montreal, PQ, Canada

[2] London Sch Econ, Dept Finance, London, England

[3] CEPR, London, England

[4] Manchester Business Sch, Dept Finance, Manchester, England

来源：

JOURNAL OF FINANCIAL ECONOMETRICS | 2025年 / 23卷 / 01期

基金：

英国经济与社会研究理事会;

关键词：

alpha; cross-sectional asset pricing; factor mimicking portfolios; factor models; pricing kernel; relative entropy; G11; G12; C13; C53; STOCHASTIC DISCOUNT FACTOR; COMMON RISK-FACTORS; PROBABILITY-DISTRIBUTIONS; ENTROPY; TESTS; RETURNS;

D O I：

10.1093/jjfinec/nbae033

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

We show that a non-parametric estimate of the pricing kernel, extracted using an information-theoretic approach, delivers smaller out-of-sample pricing errors and a better cross-sectional fit than leading multi-factor models. The information stochastic discount factor (I-SDF) identifies sources of risk not captured by standard factors, generating very large annual alphas (20-37%) and Sharpe ratio (1.1). The I-SDF extracted from a wide cross-section of equity portfolios is highly positively skewed and leptokurtic, and implies that about a third of the observed risk premia represent compensation for 2.5% tail events. The I-SDF offers a powerful benchmark relative to which competing theories and investment strategies can be evaluated.

引用

页数：40