Robust Portfolio Choice with Ambiguity and Learning About Return Predictability

Nicole Branger, Linda Sandris Larsen, Claus Munk

Research output: Contribution to journalJournal articleResearchpeer-review


We analyze the optimal stock-bond portfolio under both learning and ambiguity aversion. Stock returns are predictable by an observable and an unobservable predictor, and the investor has to learn about the latter. Furthermore, the investor is ambiguity-averse and has a preference for investment strategies that are robust to model misspecifications. We derive a closed-form solution for the optimal robust investment strategy. We find that both learning and ambiguity aversion impact the level and structure of the optimal stock investment. Suboptimal strategies resulting either from not learning or from not considering ambiguity can lead to economically significant losses.
Original languageEnglish
JournalJournal of Banking & Finance
Issue number5
Pages (from-to)1397-1411
Publication statusPublished - May 2013

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