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This bookdescribes computational financetools. It covers
fundamental numerical analysis and computational techniques, such
asoption pricing, and givesspecial attention tosimulation and
optimization. Many chapters are organized as case studies
aroundportfolio insurance and risk estimation problems. In
particular, several chapters explain optimization heuristics and
how to use them for portfolio selection and in calibration of
estimation and option pricing models. Such practical examples allow
readers to learn the steps for solving specific problems and apply
these steps to others. At the same time, the applications are
relevant enough to make the book a useful reference. Matlab and R
sample code is provided in the text and can be downloaded from the
book's website.
Computationally-intensive tools play an increasingly important role in financial decisions. Many financial problems-ranging from asset allocation to risk management and from option pricing to model calibration-can be efficiently handled using modern computational techniques. Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics. This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested empirically. This revised edition includes two new chapters, a self-contained tutorial on implementing and using heuristics, and an explanation of software used for testing portfolio-selection models. Postgraduate students, researchers in programs on quantitative and computational finance, and practitioners in banks and other financial companies can benefit from this second edition of Numerical Methods and Optimization in Finance.
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