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. 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.
General
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