Titlebook: Computational Methods in Financial Engineering; Essays in Honour of Erricos J. Kontoghiorghes,Ber? Rustem,Peter Winker Book 2008 Springer-

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0.5 References for Introduction,he sense that their removal will impact the performance of the network in the most significant way. Hence, the results have relevance to national security as well as implications for the insurance industry.

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Risk Preferences and Loss Aversion in Portfolio Optimization of a heuristic optimization approach is suggested. It is found that loss aversion has a substantial impact on what investors consider to be an efficient portfolio and that mean-variance analysis alone can be utterly misguiding.

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Classification Using Optimization: Application to Credit Ratings of Bondseral advantages including simplicity in implementation and classification robustness. The algorithm can be applied to small and large datasets. Although the approach was validated with a finance application, it is quite general and can be applied in other engineering areas.

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nuous time. In order to make the model computationally tractable, it is discretized in time and space. This approximation scheme is designed in such a way that the optimal values of the approximate problems yield bounds on the optimal value of the original problem. The convergence of the bounds is d

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0.5 References for Introduction,eliable estimations are difficult to perform and using the VaR as a constraint in portfolio optimization causes computational problems. Both problems are taken into account in the present application. First, the VaR based on estimates of the conditional covariance matrix with the ”P(pán)rincipal Componen