"This paper develops a two-country OLG model under the assumption that investors are on a Bayesian learning path. While investors from both countries receive identical information flows, domestic investors start off with less precise prior beliefs concerning foreign fundamentals. On a learning...

In the context of an international portfolio diversification problem, we find that small capitalization equity portfolios become riskier in bear markets, i.e. display negative co-skewness with other stock indices and high co-kurtosis. Because of this feature, a power utility investor ought to...

We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean-variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968-2016, we find evidence...

We present a structured portfolio optimization framework with sparse inverse covariance estimation and an attention-based LSTM network that exploits machine learning (deep learning) techniques. We shrink Wishart volatility towards a Graphical Lasso initial covariance estimator and solve the...

We examine whether simple VARs can produce empirical portfolio rules similar to those obtained under a range of multivariate Markov switching models, by studying the effects of expanding both the order of the VAR and the number/selection of predictor variables included. In a typical stock-bond...