Next Article in Journal
Convolutional Neural Network Classification of Telematics Car Driving Data
Next Article in Special Issue
Optimal Portfolio Selection in an Itô–Markov Additive Market
Previous Article in Journal
Acknowledgement to Reviewers of Risks in 2018
Previous Article in Special Issue
On the Failure to Reach the Optimal Government Debt Ceiling
Open AccessArticle

Dealing with Drift Uncertainty: A Bayesian Learning Approach

OSSIAM, 75017 Paris, France
LPSM, Université Paris Diderot, 75013 Paris, France
Author to whom correspondence should be addressed.
Received: 20 November 2018 / Revised: 25 December 2018 / Accepted: 4 January 2019 / Published: 9 January 2019
(This article belongs to the Special Issue Applications of Stochastic Optimal Control to Economics and Finance)
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 nonlearning strategy that keeps the drift constant at all times. In order to emphasize the prevalence of the Bayesian learning strategy above the nonlearning one in different situations, we experiment three different investment universes: indices of various asset classes, currencies and smart beta strategies. View Full-Text
Keywords: Bayesian learning; Markowitz problem; optimal portfolio; portfolio selection Bayesian learning; Markowitz problem; optimal portfolio; portfolio selection
Show Figures

Figure 1

MDPI and ACS Style

De Franco, C.; Nicolle, J.; Pham, H. Dealing with Drift Uncertainty: A Bayesian Learning Approach. Risks 2019, 7, 5.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop