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Data-Driven Dynamic Portfolio Selection with Vine Copula Simulation and Reinforcement Learning
We develop a data-driven approach to dynamic portfolio selection that combines vine-copula simulation with reinforcement learning. The investor has constant relative risk aversion and maximises expected terminal utility with a running penalty on the conditional value-at-risk (CVaR) of one-period los
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