Events

Economics Seminar

Authors:

• Evan Anderson
  Northern Illinois University

• Ai-Ru (Meg) Cheng
  Northern Illinois University

We propose a Bayesian averaging portfolio choice strategy with excellent out-of-sample performance. Every period a new model is born that assumes means and covariances are constant over time. Each period we estimate model parameters, update model probabilities, and compute robust portfolio choices by taking into account model uncertainty, parameter uncertainty, and non-stationarity. The portfolio choices achieve higher out-of-sample Sharpe ratios and certainty equivalents than rolling window schemes, the 1/N approach, and other leading strategies on a majority of 24 datasets.