OPTIMAL ASSET ALLOCATION WITH MULTIVARIATE BAYESIAN DYNAMIC LINEAR MODELS

• 成果类型: Article
• 署名作者: Fisher, Jared D.; Pettenuzzo, Davide; Carvalho, Carlos M.
• 署名单位: University of California System; University of California Berkeley; Brandeis University; University of Texas System; University of Texas Austin
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/19-AOAS1303
• 发表日期: 2020
• 页码: 299-338
• 关键词: equity premium; vector autoregressions; returns; predictability; standard

摘要

We introduce a fast, closed-form, simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian dynamic linear models of West and Harrison (Bayesian Forecasting and Dynamic Models (1997) Springer), and it can objectively determine, through a fully automated procedure, both the optimal set of regressors to include in the predictive system and the degree to which the model coefficients, volatilities and covariances should vary over time. When applied to a portfolio of five stock and bond returns, we find that our method leads to large forecast gains, both in statistical and economic terms. In particular, we find that relative to a standard no-predictability benchmark, the optimal combination of predictors, stochastic volatility and time-varying covariances increases the annualized certainty equivalent returns of a leverage-constrained power utility investor by more than 500 basis points.