A dynamic programming framework for VaR-constrained portfolio choice under heavy-tailed returns, but the numerical implementation fits prices instead of returns and omits the actual optimization.
Markov Decision Processes under Ambiguity
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abstract
We consider statistical Markov Decision Processes where the decision maker is risk averse against model ambiguity. The latter is given by an unknown parameter which influences the transition law and the cost functions. Risk aversion is either measured by the entropic risk measure or by the Average Value at Risk. We show how to solve these kind of problems using a general minimax theorem. Under some continuity and compactness assumptions we prove the existence of an optimal (deterministic) policy and discuss its computation. We illustrate our results using an example from statistical decision theory.
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Discrete time portfolio optimisation managing value at risk under heavy tail return distribution
A dynamic programming framework for VaR-constrained portfolio choice under heavy-tailed returns, but the numerical implementation fits prices instead of returns and omits the actual optimization.