"Sequential learning, predictive regressions, and optimal portfolio returns"
Publication type: Working paper
This paper develops particle filtering and learning for sequential inference in empirical asset pricing models. We provide a computationally feasible toolset for sequential parameter learning, hypothesis testing, and model monitoring, incorporating multiple observable variables, unobserved stochastic volatility, and unobserved "drifting" regression coefficients. Sequential inference allows us to observe how the views of economic decision makers evolve in real time. Empirically, we analyze time series predictability of equity returns, using both the traditional dividend yield and net payout yield, which incorporates issuances and repurchases. We find that the data rejects the traditional model for both predictors, in favor of models with drifting coefficients and stochastic volatility. Evidence for predictability is stronger in models that feature stochastic volatility. We study the optimal portfolio allocation problem under parameter, state variable, and model uncertainty, and show that the Bayesian portfolios are more stable and have better out-of-sample performance than rolling regressions.
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