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Hardcover. Zustand: Good. 1. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. 2013. 1st Edition. Hardcover. A comprehensive introduction to various numerical methods used in computational finance today Quantitative skills are a prerequisite for anyone working in finance or beginning a career in the field, as well as risk managers. Series: Wiley Finance. Num Pages: 336 pages, illustrations. BIC Classification: KF; UFM. Category: (P) Professional & Vocational. Dimension: 249 x 179 x 24. Weight in Grams: 740. . . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - A comprehensive introduction to various numerical methods used in computational finance todayQuantitative skills are a prerequisite for anyone working in finance or beginning a career in the field, as well as risk managers. A thorough grounding in numerical methods is necessary, as is the ability to assess their quality, advantages, and limitations. This book offers a thorough introduction to each method, revealing the numerical traps that practitioners frequently fall into. Each method is referenced with practical, real-world examples in the areas of valuation, risk analysis, and calibration of specific financial instruments and models. It features a strong emphasis on robust schemes for the numerical treatment of problems within computational finance. Methods covered include PDE/PIDE using finite differences or finite elements, fast and stable solvers for sparse grid systems, stabilization and regularization techniques for inverse problems resulting from the calibration of financial models to market data, Monte Carlo and Quasi Monte Carlo techniques for simulating high dimensional systems, and local and global optimization tools to solve the minimization problem.