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

Arbeitspapier

We summarize the general combination approach 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 11-082/4

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General ; Forecasting Models; Simulation Methods ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Subject: Density Forecast Combination ; Stock data ; Börsenkurs ; Prognoseverfahren ; Bayes-Statistik ; ARCH-Modell ; Niederlande

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Billio, Monica ; Casarin, Roberto ; Ravazzolo, Francesco ; van Dijk, Herman K.
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2011

Further information

• Handle: http://hdl.handle.net/10419/87064
• Last update: 10.03.2025, 11:42 AM CET