{"product_id":"handbook-in-monte-carlo-simulation-isbn-9780470531112","title":"Handbook in Monte Carlo Simulation","description":"\u003cp\u003eAn accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics\u003c\/p\u003e \u003cp\u003eProviding readers with an in-depth and comprehensive guide, the \u003ci\u003eHandbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics \u003c\/i\u003epresents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization.\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eHandbook in Monte Carlo Simulation \u003c\/i\u003efeatures:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAn introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials\u003c\/li\u003e \u003cli\u003eCarefully crafted examples in order to spot potential pitfalls and drawbacks of each approach\u003c\/li\u003e \u003cli\u003eAn accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods\u003c\/li\u003e \u003cli\u003eNumerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe \u003ci\u003eHandbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics \u003c\/i\u003eis a complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation.\u003c\/p\u003e  \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Overview and Motivation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Monte Carlo Methods\u003c\/b\u003e \u003cb\u003e3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Historical origin of Monte Carlo simulation 4\u003c\/p\u003e \u003cp\u003e1.2 Monte Carlo Simulation vs. Monte Carlo Sampling 7\u003c\/p\u003e \u003cp\u003e1.3 System dynamics and the mechanics of Monte Carlo simulation 10\u003c\/p\u003e \u003cp\u003e1.4 Simulation and optimization 21\u003c\/p\u003e \u003cp\u003e1.5 Pitfalls in Monte Carlo simulation 30\u003c\/p\u003e \u003cp\u003e1.6 Software tools for Monte Carlo simulation 35\u003c\/p\u003e \u003cp\u003e1.7 Prerequisites 37\u003c\/p\u003e \u003cp\u003eFor further reading 38\u003c\/p\u003e \u003cp\u003eChapter References 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Numerical Integration Methods\u003c\/b\u003e \u003cb\u003e41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Classical quadrature formulae 43\u003c\/p\u003e \u003cp\u003e2.2 Gaussian quadrature 48\u003c\/p\u003e \u003cp\u003e2.3 Extension to higher dimensions: Product rules 53\u003c\/p\u003e \u003cp\u003e2.4 Alternative approaches for high-dimensional integration 55\u003c\/p\u003e \u003cp\u003e2.5 Relationship with moment matching 67\u003c\/p\u003e \u003cp\u003e2.6 Numerical integration in R 69\u003c\/p\u003e \u003cp\u003eFor further reading 71\u003c\/p\u003e \u003cp\u003eChapter References 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Input Analysis: Modeling and Estimation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Stochastic Modeling in Finance and Economics\u003c\/b\u003e \u003cb\u003e75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introductory examples 77\u003c\/p\u003e \u003cp\u003e3.2 Some common probability distributions 86\u003c\/p\u003e \u003cp\u003e3.3 Multivariate distributions: Covariance and correlation 111\u003c\/p\u003e \u003cp\u003e3.4 Modeling dependence with copulae 127\u003c\/p\u003e \u003cp\u003e3.5 Linear regression models: a probabilistic view 136\u003c\/p\u003e \u003cp\u003e3.6 Time series models 137\u003c\/p\u003e \u003cp\u003e3.7 Stochastic differential equations 158\u003c\/p\u003e \u003cp\u003e3.8 Dimensionality reduction 177\u003c\/p\u003e \u003cp\u003eS3.1 Risk-neutral derivative pricing 190\u003c\/p\u003e \u003cp\u003eS3.1.1 Option pricing in the binomial model 192\u003c\/p\u003e \u003cp\u003eS3.1.2 A continuous-time model for option pricing: The Black–Scholes–Merton formula 194\u003c\/p\u003e \u003cp\u003eS3.1.3 Option pricing in incomplete markets 199\u003c\/p\u003e \u003cp\u003eFor further reading 202\u003c\/p\u003e \u003cp\u003eChapter References 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Estimation and Fitting\u003c\/b\u003e \u003cb\u003e205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Basic inferential statistics in R 207\u003c\/p\u003e \u003cp\u003e4.2 Parameter estimation 215\u003c\/p\u003e \u003cp\u003e4.3 Checking the fit of hypothetical distributions 224\u003c\/p\u003e \u003cp\u003e4.4 Estimation of linear regression models by ordinary least squares 229\u003c\/p\u003e \u003cp\u003e4.5 Fitting time series models 232\u003c\/p\u003e \u003cp\u003e4.6 Subjective probability: the Bayesian view 235\u003c\/p\u003e \u003cp\u003eFor further reading 244\u003c\/p\u003e \u003cp\u003eChapter References 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Sampling and Path Generation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Random Variate Generation\u003c\/b\u003e \u003cb\u003e249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 The structure of a Monte Carlo simulation 250\u003c\/p\u003e \u003cp\u003e5.2 Generating pseudo-random numbers 252\u003c\/p\u003e \u003cp\u003e5.3 The inverse transform method 263\u003c\/p\u003e \u003cp\u003e5.4 The acceptance–rejection method 265\u003c\/p\u003e \u003cp\u003e5.5 Generating normal variates 269\u003c\/p\u003e \u003cp\u003e5.6 Other ad hoc methods 274\u003c\/p\u003e \u003cp\u003e5.7 Sampling from copulae 276\u003c\/p\u003e \u003cp\u003eFor further reading 277\u003c\/p\u003e \u003cp\u003eChapter References 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Sample Path Generation for Continuous-Time Models\u003c\/b\u003e \u003cb\u003e281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Issues in path generation 282\u003c\/p\u003e \u003cp\u003e6.2 Simulating geometric Brownian motion 287\u003c\/p\u003e \u003cp\u003e6.3 Sample paths of short-term interest rates 298\u003c\/p\u003e \u003cp\u003e6.4 Dealing with stochastic volatility 306\u003c\/p\u003e \u003cp\u003e6.5 Dealing with jumps 308\u003c\/p\u003e \u003cp\u003eFor further reading 310\u003c\/p\u003e \u003cp\u003eChapter References 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Output Analysis and Efficiency Improvement\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Output Analysis\u003c\/b\u003e \u003cb\u003e315\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Pitfalls in output analysis 317\u003c\/p\u003e \u003cp\u003e7.2 Setting the number of replications 323\u003c\/p\u003e \u003cp\u003e7.3 A world beyond averages 325\u003c\/p\u003e \u003cp\u003e7.4 Good and bad news 327\u003c\/p\u003e \u003cp\u003eFor further reading 327\u003c\/p\u003e \u003cp\u003eChapter References 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Variance Reduction Methods\u003c\/b\u003e \u003cb\u003e329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Antithetic sampling 330\u003c\/p\u003e \u003cp\u003e8.2 Common random numbers 336\u003c\/p\u003e \u003cp\u003e8.3 Control variates 337\u003c\/p\u003e \u003cp\u003e8.4 Conditional Monte Carlo 341\u003c\/p\u003e \u003cp\u003e8.5 Stratified sampling 344\u003c\/p\u003e \u003cp\u003e8.6 Importance sampling 350\u003c\/p\u003e \u003cp\u003eFor further reading 363\u003c\/p\u003e \u003cp\u003eChapter References 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Low-Discrepancy Sequences\u003c\/b\u003e \u003cb\u003e365\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Low-discrepancy sequences 366\u003c\/p\u003e \u003cp\u003e9.2 Halton sequences 367\u003c\/p\u003e \u003cp\u003e9.3 Sobol low-discrepancy sequences 374\u003c\/p\u003e \u003cp\u003e9.4 Randomized and scrambled low-discrepancy sequences 379\u003c\/p\u003e \u003cp\u003e9.5 Sample path generation with low-discrepancy sequences 381\u003c\/p\u003e \u003cp\u003eFor further reading 385\u003c\/p\u003e \u003cp\u003eChapter References 385\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Miscellaneous Applications\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimization\u003c\/b\u003e \u003cb\u003e389\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Classification of optimization problems 390\u003c\/p\u003e \u003cp\u003e10.2 Optimization model building 405\u003c\/p\u003e \u003cp\u003e10.3 Monte Carlo methods for global optimization 412\u003c\/p\u003e \u003cp\u003e10.4 Direct search and simulation-based optimization methods 416\u003c\/p\u003e \u003cp\u003e10.5 Stochastic programming models 420\u003c\/p\u003e \u003cp\u003e10.6 Scenario generation and Monte Carlo methods for stochastic programming 428\u003c\/p\u003e \u003cp\u003e10.7 Stochastic dynamic programming 433\u003c\/p\u003e \u003cp\u003e10.8 Numerical dynamic programming 440\u003c\/p\u003e \u003cp\u003e10.9 Approximate dynamic programming 451\u003c\/p\u003e \u003cp\u003eFor further reading 453\u003c\/p\u003e \u003cp\u003eChapter References 453\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Option Pricing\u003c\/b\u003e \u003cb\u003e455\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 European-style multidimensional options in the BSM world 456\u003c\/p\u003e \u003cp\u003e11.2 European-style path-dependent options in the BSM world 462\u003c\/p\u003e \u003cp\u003e11.3 Pricing options with early exercise features 475\u003c\/p\u003e \u003cp\u003e11.4 A look outside the BSM world 487\u003c\/p\u003e \u003cp\u003e11.5 Pricing interest-rate derivatives 490\u003c\/p\u003e \u003cp\u003eFor further reading 497\u003c\/p\u003e \u003cp\u003eChapter References 498\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Sensitivity Estimation\u003c\/b\u003e \u003cb\u003e501\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Estimating option greeks by finite differences 503\u003c\/p\u003e \u003cp\u003e12.2 Estimating option greeks by pathwise derivatives 509\u003c\/p\u003e \u003cp\u003e12.3 Estimating option greeks by the likelihood ratio method 513\u003c\/p\u003e \u003cp\u003eFor further reading 517\u003c\/p\u003e \u003cp\u003eChapter References 518\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Risk Measurement and Management\u003c\/b\u003e \u003cb\u003e519\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 What is a risk measure? 520\u003c\/p\u003e \u003cp\u003e13.2 Quantile-based risk measures: value at risk 522\u003c\/p\u003e \u003cp\u003e13.3 Monte Carlo methods for V@R 533\u003c\/p\u003e \u003cp\u003e13.4 Mean-risk models in stochastic programming 537\u003c\/p\u003e \u003cp\u003e13.5 Simulating delta-hedging strategies 540\u003c\/p\u003e \u003cp\u003e13.6 The interplay of financial and nonfinancial risks 546\u003c\/p\u003e \u003cp\u003eFor further reading 548\u003c\/p\u003e \u003cp\u003eChapter References 548\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Markov Chain Monte Carlo and Bayesian Statistics\u003c\/b\u003e \u003cb\u003e551\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 An introduction to Markov chains 552\u003c\/p\u003e \u003cp\u003e14.2 The Metropolis–Hastings algorithm 555\u003c\/p\u003e \u003cp\u003e14.3 A re-examination of simulated annealing 558\u003c\/p\u003e \u003cp\u003eFor further reading 560\u003c\/p\u003e \u003cp\u003eChapter References 561\u003c\/p\u003e \u003cp\u003eIndex 563\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePAOLO BRANDIMARTE\u003c\/b\u003e is Full Professor of Quantitative Methods for Finance and Logistics in the Department of Mathematical Sciences at Politecnico di Torino in Italy. He has extensive teaching experience in engineering and economics faculties, including master’s- and PhD-level courses. Dr. Brandimarte is the author or coauthor of \u003ci\u003eIntroduction to Distribution Logistics, Quantitative Methods: An Introduction for Business Management\u003c\/i\u003e, and \u003ci\u003eNumerical Methods in Finance and Economics: A MATLAB-Based Introduction, Second Edition\u003c\/i\u003e, all published by Wiley.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAN ACCESSIBLE TREATMENT OF MONTE CARLO METHODS, TECHNIQUES, AND APPLICATIONS IN THE FIELD OF FINANCE AND ECONOMICS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProviding readers with an in-depth and comprehensive guide, the \u003ci\u003eHandbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics\u003c\/i\u003e presents a timely account of the applications of Monte Carlo methods in financial engineering and economics. Written by an international leading expert in the field, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applications of Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction and motivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysis and variance reduction; and applications ranging from option pricing and risk management to optimization.\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eHandbook in Monte Carlo Simulation\u003c\/i\u003e features:\u003c\/p\u003e \u003cp\u003e\u003cb\u003e■\u003c\/b\u003e An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials\u003c\/p\u003e \u003cp\u003e\u003cb\u003e■\u003c\/b\u003e Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach\u003c\/p\u003e \u003cp\u003e\u003cb\u003e■\u003c\/b\u003e An accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods\u003c\/p\u003e \u003cp\u003e\u003cb\u003e■\u003c\/b\u003e Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eHandbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics\u003c\/i\u003e is a complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989324644581,"sku":"NP9780470531112","price":166.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470531112.jpg?v=1761783672","url":"https:\/\/k12savings.com\/products\/handbook-in-monte-carlo-simulation-isbn-9780470531112","provider":"K12savings","version":"1.0","type":"link"}