Dealing with Drift Uncertainty: A Bayesian Learning Approach

19 Pages Posted: 10 Dec 2018

Date Written: November 15, 2018

Abstract

One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain level of confidence, we focus on drift uncertainty in this paper. Building on filtering techniques and learning methods, we use a Bayesian learning approach to solve the Markowitz problem and provide a simple and practical procedure to implement optimal strategy. To illustrate the value added of using the optimal Bayesian learning strategy, we compare it with an optimal non-learning strategy that keeps the drift constant at all times. In order to emphasize the prevalence of the Bayesian learning strategy above the non-learning one in different situations, we experiment three different investment universes: indices of various asset classes, currencies and smart beta strategies.

Keywords: Bayesian learning, optimal portfolio, Markowitz problem, portfolio selection

JEL Classification: C11, C61, G11

Suggested Citation

De Franco, Carmine and Nicolle, Johann and Pham, Huyen, Dealing with Drift Uncertainty: A Bayesian Learning Approach (November 15, 2018). Available at SSRN: https://ssrn.com/abstract=3285248 or http://dx.doi.org/10.2139/ssrn.3285248

Carmine De Franco

OSSIAM ( email )

80 Avenue de la Grande Armée
Paris, 75017
France

Johann Nicolle (Contact Author)

OSSIAM ( email )

80 Avenue de la Grande Armée
Paris, 75017
France

Huyen Pham

Université Paris VII Denis Diderot ( email )

Batiment Sophie Germain 5 rue Thomas Mann
Paris, 75205
France

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