Bayesian Combinations of Stock Price Predictions with an Application to the Amsterdam Exchange Index

Abstract

We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic auto-regressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative purposes we apply it to combine White Noise and GARCH models to forecast the Amsterdam Exchange index and use the combined predictive forecasts in an investment asset allocation exercise.

Keywords: Density forecast combination, stock data

JEL Classification: C11, C15, C53, E37