Titlebook: Dynamic Stochastic Optimization; Kurt Marti,Yuri Ermoliev,Georg Pflug Conference proceedings 2004 Springer-Verlag Berlin Heidelberg 2004 A

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Optimal Solutions for Undiscounted Variance Penalized Markov Decision Chainsd per unit time, may be quite insufficient to characterize the problem from the point of the decision maker. To this end it may be necessary to select a criterion that reflects the variability-risk features of the problem. Perhaps the best known approach stems from the classical work of Markowitz [.

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Optimal Stopping Problem and Investment Models is based on connection between boundary problem for diffusion processes and Dirichlet problem for PDE of an elliptic type. The solution of a Dirichlet problem is considered as a functional of the available continuation regions. The optimization of this functional will be carried out by variational

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Estimating LIBOR/Swaps Spot-Volatilities: the EpiVolatility Modelilities. This estimation is carried out using either the market price of interest rate caps or floors, as quoted on broker screens. In particular, we are interested in being able to estimate spot volatilities for time steps of arbitrary length. Further, this technique does not assume any particular

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Structured Products for Pension Fundsinvolves econometric modelling, economic scenario generation, generic methods of solving optimization problems and modelling of required risk tolerances. In nearly all the historical backtests using data over roughly the past decade the system described (with transactions costs taken into account) o

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Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) for Robotses of the model parameters, and to correct then the resulting increasing deviation of the actual trajectory or the actual performance of the system from the prescribed trajectory, from the prescribed values of performance, resp., by on-line measurement and control actions. However, on-line measureme

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Stochastic Optimization of Risk Functions via Parametric Smoothingften requires the solution of dynamic stochastic optimization problems with discontinuous indicator functions of such events as ruin, underestimating costs and overestimating benefits. The available optimization techniques, in particular formulas for derivatives of risk functions, may not be applica

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Optimization under Uncertainty using MomentumWhat emerges is a constrained, stochastic, second-order process. Some friction feeds into and stabilizes myopic approximations. Convergence obtains under weak and natural conditions, an important one being that accumulated marginal payoff remains bounded above.

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