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.