Three concepts: stochastic discount factors, multi-beta pricing and mean-variance efficiency, are at the core of modern empirical asset pricing. This chapter reviews these paradigms and the relations among them, concentrating on conditional asset-pricing models where lagged variables serve as...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

We investigate changepoints in the cross-section of stock returns using an ensemble of dedicated unsupervised learning methods. Our large-scale study reveals a sustained incidence of changepoints in the mean, variance, and distribution. This finding is robust to the choice of the changepoint...

We study structural models of stochastic discount factors and explore alternative methods of estimating such models using data on macroeconomic risk and asset returns. Particular attention is devoted to recursive utility models in which risk aversion can be modified without altering...

The purpose of this paper is to propose a general econometric approach to no-arbitrage asset pricing modelling based on three main ingredients: (i) the historical discrete-time dynamics of the factor representing the information, (ii) the Stochastic Discount Factor (SDF), and (iii) the...

We introduce a class of interpretable tree-based models (P-Tree) for analyzing (unbalanced) panel data, with iterative and global (instead of recursive and local) split criteria. We apply P-Tree to split the cross section of asset returns under the no-arbitrage condition, generating a stochastic...

A tree-structured linear and quantile regression framework is proposed for the analysis and modeling of equity market returns. The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian...

In an exchange economy with recursive preferences (Epstein and Zin, 1989), we propose a novel nonparametric generalized method of moment (GMM) series approach to estimate unknown policy functions which are recursively specified in a system of nonlinear conditional expectation models...

Moment-based estimation often yields instable estimates, such as the RRA (relative risk aversion) estimate in consumption-based asset pricing. This paper establishes novel theoretical results for the ESP (empirical saddlepoint) approximation, and then use them to investigate this instability. We...

Trading is not continuous, leading to asynchronous trading times for different assets. This paper analyses the effect of asynchronous trading on an asset’s beta and evaluates its implication for the asset pricing literature. It shows that estimates are heavily downward-biased, with estimated...