This textbook teaches readers steps for solving specific problems in finance and applying them to other problems. After a short introduction about numerical analysis, the authors devote two sections to pricing financial models and simulation to prepare readers for the book's core subject, optimization. Assuming that a model is only as good as its results, they provide a comprehensive overview and treatment of heuristic optimization techniques, only briefly touching upon standard methods. Arguing against judging models by the elegance of their math or whether it fits nicely into a theoretical framework, they advocate a pragmatic approach: implementing models to gain intuition about them. They provide sample code in the text, primarily Matlab and R, and offer code for download at the book's website. This practical textbook can serve equally well as a self-contained desk reference. Shows ways to build and implement tools that help test ideas Focuses on the application of heuristics; standard methods receive limited attention Presents as separate chapters problems from portfolio optimization, estimation of econometric models, and calibration of option pricing models

Auteur

University of Geneva, Switzerland

Résumé
Describes computational finance tools. This title covers fundamental numerical analysis and computational techniques, such as option pricing, and gives special attention to simulation and optimization. It shows ways to build and implement tools that help test ideas. It focuses on the application of heuristics.

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