The formulation of the Black-Litterman model as a Bayesian mixed estimation approach allows for computing the posterior expected returns taking into account the views of investor on future returns. When the views turn out to be wrong, the resulting portfolio may lead to losses. Sometimes, it may...

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...

Long-run risk models, a cornerstone in the macro-finance literature for their ability to capture key asset price phenomena, are known to entail implausibly high levels of timing and risk premia. Our paper resolves this puzzle by considering consumption of durable goods in addition to that of...

Financial markets provide a natural quantitative lab for understanding some of the most advanced human behaviours. Among them is the invention and use of mathematical tools known as financial instruments. Besides money, the two most fundamental financial instruments are bonds and equities. More...

This paper develops a Bayesian methodology to compare asset pricing models containing non-traded factors and principal components. Existing comparison procedures are inadequate when models include such factors due to estimation uncertainties in mimicking portfolios and return covariances....

In light of the power problems of statistical tests and undisciplined use of alpha-based statistics to compare models, this paper proposes a unified set of distance-based performance metrics, derived as the square root of the sum of squared alphas and squared standard errors. The Bayesian...

We perform an out-of-sample comparison of linear factor asset pricing models from an economic perspective under predictability. We assess the economic value added of several factor models when a Bayesian investor is faced with a portfolio allocation problem whereby each model imposes...

This paper introduces a new methodology to estimate time‐varying alphas and betas in conditional factor models, which allows substantial flexibility in a time‐varying framework. To circumvent problems associated with the previous approaches, we introduce a Bayesian time‐varying parameter...

We propose a unified set of distance-based performance metrics that address the power and extreme-error problems inherent in traditional measures for asset-pricing tests. From a Bayesian perspective, the distance metrics coherently incorporate both pricing errors and their standard errors....

Sparse models, though long preferred and pursued by social scientists, can be ineffective or unstable relative to large models, for example, in economic predictions (Giannone et al., 2021). To achieve sparsity for economic interpretation while exploiting big data for superior empirical...