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Markov Decision Processes with Risk-Sensitive Criteria: An Overview
The term 'risk-sensitive' refers here to the use of the Optimized Certainty Equivalent as a means to measure expectation and risk.
Optimal investment in ambiguous financial markets with learning
Based on our theoretical results, we are able to shed light on the impact of the prior drift distribution as well as the consequences of ambiguity preferences via the transfer to an adjusted drift distribution, i. e. we are able to explain the interaction of risk and ambiguity preferences.
Optimal investment under partial information and robust VaR-type constraint
Partial information is characterized by the fact that the stock price itself is observable by the optimizing financial institution, but the outcome of the market price of the risk $\theta$ is unknown to the institution.
Bayesian optimal investment and reinsurance with dependent financial and insurance risks
What turns out to be very surprising is that numerical results indicate that even a minimal dependence which is created in this model has a huge impact on the control in the sense that the insurer is much more prudent then.
Minimizing Spectral Risk Measures Applied to Markov Decision Processes
We study the minimization of a spectral risk measure of the total discounted cost generated by a Markov Decision Process (MDP) over a finite or infinite planning horizon.
Optimization and Control Risk Management 90C40 (Primary) 91G70, 91G05 (Secondary)
Portfolio Optimization in Fractional and Rough Heston Models
We consider a fractional version of the Heston volatility model which is inspired by [16].
