Relative Entropy and Minimum-Variance Pricing Kernel in Asset Pricing Model Evaluation.

Recent literature shows that many testing procedures used to evaluate asset pricing models result in spurious rejection probabilities. Model misspecification, the strong factor structure of test assets, or skewed test statistics largely explain this. In this paper we use the relative entropy of pricing kernels to provide an alternative framework for testing asset pricing models. Building on the fact that the law of one price guarantees the existence of a valid pricing kernel, we study the relationship between the mean-variance efficiency of a model's factor-mimicking portfolio, as measured by the cross-sectional generalized least squares (GLS) R 2 statistic, and the relative entropy of the pricing kernel, as determined by the Kullback-Leibler divergence. In this regard, we suggest an entropy-based decomposition that accurately captures the divergence between the factor-mimicking portfolio and the minimum-variance pricing kernel resulting from the Hansen-Jagannathan bound. Our results show that, although GLS R 2 statistics and relative entropy are strongly correlated, the relative entropy approach allows us to explicitly decompose the explanatory power of the model into two components, namely, the relative entropy of the pricing kernel and that corresponding to its correlation with asset returns. This makes the relative entropy a versatile tool for designing robust tests in asset pricing.

Impact of consumer confidence on the expected returns of the Tokyo Stock Exchange: A comparative analysis of consumption and production-based asset pricing models.

Most single-factor and multifactor asset pricing models constitute special cases of the consumption-based asset pricing theory, in which investors' marginal utility is the key determinant of asset prices. However, in recent years, production-based asset pricing models have been extraordinarily successful in correctly pricing a wide range of anomaly portfolios that are typically mispriced in previous research. In parallel, research on conditioning information has contributed to significantly improve the performance of classic consumption-based asset pricing models. On this basis, in this paper we conduct an in-depth research on the performance of consumption and production-based asset pricing models on the Tokyo Stock Exchange, for the period from 1992 to 2018, in order to test to what extent consumer confidence helps consumption models to correctly capture shifts in the investment opportunity set of investors. To overcome the constraints imposed by the periodicity of macroeconomic data, we use a factor-mimicking portfolio approach that allows us to test the performance of the models into consideration at different frequencies. Our results suggest that the consumer confidence index for Japan helps consumption-based asset pricing models outperform production-based models for different anomaly portfolios. Conversely, in those cases where consumption models perform worse, the production models also perform poorly. These results help to partially reconcile the results provided by the consumption and production models, and constitute a step forward for the purpose of identifying the fundamental risk factors that drive asset prices.