https://cnrs.hal.science/hal-03907148Pagès, GillesGillesPagèsLPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche ScientifiqueNumerical ProbabilityNumerical ProbabilityHAL CCSD2018Monte Carlo methodvariance reductionQuasi-Monte Carlo methodstochastic differential equationsdiscretization schemesEuler schemeMilstein schemeoptimal vector quantizationstochastic approximationmultilevel extrapolation methodsRomberg extrapolation methodspricing of derivative productsgreeks sensitivity computationtangent process and log-likelihood methodMalliavin Monte Carlorisk measuresValue-at-Risk (conditional)American optionsleast squares regression methodsquantization schemes[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Pagès, GillesEd. Sheldon Axler et al.2022-12-19 20:46:242023-03-24 14:53:302022-12-19 20:46:24enBooks1This textbook provides a self-contained introduction to numerical methods in probability with a focus on applications to finance.Topics covered include the Monte Carlo simulation (including simulation of random variables, variance reduction, quasi-Monte Carlo simulation, and more recent developments such as the multilevel paradigm), stochastic optimization and approximation, discretization schemes of stochastic differential equations, as well as optimal quantization methods. The author further presents detailed applications to numerical aspects of pricing and hedging of financial derivatives, risk measures (such as value-at-risk and conditional value-at-risk), implicitation of parameters, and calibration.Aimed at graduate students and advanced undergraduate students, this book contains useful examples and over 150 exercises, making it suitable for self-study.