Comparison and robustification of Bayes and Black-Litterman models

Katrin Schöttle, Ralf Werner, Rudi Zagst

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

For determining an optimal portfolio allocation, parameters representing the underlying market-characterized by expected asset returns and the covariance matrix-are needed. Traditionally, these point estimates for the parameters are obtained from historical data samples, but as experts often have strong opinions about (some of) these values, approaches to combine sample information and experts' views are sought for. The focus of this paper is on the two most popular of these frameworks-theBlack-Litterman model and theBayes approach.We will prove that-from the point of traditional portfolio optimization-the Black-Litterman is just a special case of the Bayes approach. In contrast to this, we will show that the extensions of both models to the robust portfolio framework yield two rather different robustified optimization problems.

Original languageEnglish
Pages (from-to)453-475
Number of pages23
JournalMathematical Methods of Operations Research
Volume71
Issue number3
DOIs
StatePublished - Jun 2010

Keywords

  • Bayes model
  • Black-Litterman model
  • Portfolio optimization
  • Robust optimization

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