Bayesian emulation for multi-step optimization in decision problems

Journal Article (Journal Article)

We develop a Bayesian approach to computational solution of multistep optimization problems, highlighted in the example of financial portfolio decisions. The approach involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portfolio analysis using classes of economically and psychologically relevant multi-step ahead portfolio utility functions. Studies with multivariate currency time series illustrate the approach and show some of the practical utility and benefits of the Bayesian emulation methodology.

Full Text

Duke Authors

Cited Authors

  • Irie, K; West, M

Published Date

  • January 1, 2019

Published In

Volume / Issue

  • 14 / 1

Start / End Page

  • 137 - 160

Electronic International Standard Serial Number (EISSN)

  • 1931-6690

International Standard Serial Number (ISSN)

  • 1936-0975

Digital Object Identifier (DOI)

  • 10.1214/18-BA1105

Citation Source

  • Scopus