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Activity Number: 663
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #320218 View Presentation
Title: Bayesian Dynamic Linear Models for Strategic Asset Allocation
Author(s): Jared Fisher* and Carlos Carvalho and Davide Pettenuzzo
Companies: The University of Texas McCombs School of Business and The University of Texas and Brandeis University
Keywords: Return Prediction ; Strategic Asset Allocation ; Bayesian Econometrics ; Time-varying Parameters ; Stochastic Volatility

Using Bayesian dynamic linear models, we provide a method of predicting risk premia that incorporates time-varying parameters, stochastic volatility and variance discounting. These models are capable of jointly predicting the risk premia for multiple assets which permits the strategic allocation of these assets in a portfolio. This methodology is demonstrated for a portfolio containing a stock index, bond index, and a risk-free asset. We average across many models with different combinations of predictors and discount factors. Our averaged models show statistical and economic improvements in out-of-sample predictability compared to models incorporating subsets of the aforementioned features as well as the baseline historic mean model.

Authors who are presenting talks have a * after their name.

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