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

摘要

This paper deals with the problem of combining predictive densities for financial series. We summarize the general combination approach based on a Bayesian state space representation of the predictive densities and of the combination scheme which allows for incomplete model space proposed by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive 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.