Dealing with Drift Uncertainty: A Bayesian Learning Approach
Carmine De Franco (),
Johann Nicolle () and
Huyên Pham ()
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Carmine De Franco: OSSIAM, 75017 Paris, France
Johann Nicolle: OSSIAM, 75017 Paris, France
Huyên Pham: LPSM, Université Paris Diderot, 75013 Paris, France
Risks, 2019, vol. 7, issue 1, 1-18
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.
Keywords: Bayesian learning; Markowitz problem; optimal portfolio; portfolio selection (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:7:y:2019:i:1:p:5-:d:196215
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