Titlebook: Computational Methods in Decision-Making, Economics and Finance; Erricos John Kontoghiorghes,Berc Rustem,Stavros Si Book 2002 Springer Sci

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HARP

https://doi.org/10.1007/978-1-4757-3613-7Monte Carlo Simulation; Simulation; Stochastic Optimization; Stochastic Programming; algorithms; calculus

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Working-Memory

Vldl379

Impact of Substracte on Performance,ario tree. The mean or variance of total wealth at the end of the planning horizon can be optimised by solving either a linear stochastic program or a quadratic stochastic program, respectively; solution of many almost identical quadratic stochastic programs yields points describing the Markowitz ef

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https://doi.org/10.1007/b100751 at some discrete points of time. The uncertain evolution of future cashflows and of the capital markets is modeled with stochastic processes which have to be discretized in order to formulate a deterministic equivalent. We use the barycentric approximation which has proved to be a promising discret

Innovative

Impact of Substracte on Performance,lysis, or min-max. Robustness is ensured by considering the the optimal strategy in view of multiple scenarios generated and evaluating the portfolio corresponding to the best performance, simultaneously with the worst-case scenario. The robust property follows from the fact that the resulting strat

木质

Impact of Substracte on Performance,timization problem. We consider different forms of calculating the mean and semivariance of the tracking error. It is desired to minimize an objective function defined as a convex combination of the risk function minus the expected return of the tracking error.

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