In this article, we link the realized accuracy of predictive panels to changes in distributions that occur between the training (in-sample) phase and the testing (out-of-sample) phase. We obtain polynomial upper bounds for the loss of accuracy between training and testing. We model covariate...

We extend the model of Anufriev and Bottazzi (2010) to the case with many assets. We show that under the procedural equilibrium, all assets with nonzero aggregate demand must have the same price returns. Heterogeneity in price returns appears when some assets face zero demand. In this case, the...

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns as dependent variables. In addition, we...

This paper considers the problem of measuring the exposure to dependence risk carried by a portfolio with an arbitrary number of two-asset derivative contracts. We develop a worst-case risk measure computed over a set of dependence scenarios within a divergence restricted region. The set of...

This paper deals with the problem of modelling the volatility of futures prices in agricultural markets. We develop a multi-factor model in which the stochastic volatility dynamics incorporate a seasonal component. In addition, a maturity dependent damping term accounts for the Samuelson effect....