{"product_id":"handbook-in-monte-carlo-simulation-applications-in-financial-engineering-risk-management-and-economics-hardback-9780470531112","title":"Handbook in Monte Carlo Simulation; Applications in Financial Engineering, Risk Management, and Economics (Hardback) 9780470531112","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eHandbook in Monte Carlo Simulation\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eApplications in Financial Engineering, Risk Management, and Economics\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePaolo Brandimarte (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470531112, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 6 June 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e688 pages, Tables: 150 B\u0026amp;W, 0 Color; Graphs: 75 B\u0026amp;W, 0 Color\u003cbr\u003e25.7 x 18.3 x 4.1 cm, 1.361 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\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\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\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\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand 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