Paolo Brandimarte

Handbook in Monte Carlo Simulation

Applications in Financial Engineering, Risk Management, and Economics

• Gebundenes Buch

Produktbeschreibung

Concentrating primarily on easily displayed theories and methodologies of Monte Carlo simulation, this authoritative book goes wider and deeper than any other and includes timely applications to the fields of financial engineering, risk management, and economics. Written by a well-known, international expert in the field, the book includes topics such as random number and variate generation, input modeling with real data analysis for adequate fit, Bayesian MCMC, and more. It is a handy reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering

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An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics

Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents 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.

The Handbook in Monte Carlo Simulation features:
An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials
Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach
An accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods
Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation

The Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics 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.

Produktdetails

• Wiley Handbooks in Financial Engineering and Econometrics
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 688
• Erscheinungstermin: 5. Mai 2014
• Englisch
• Abmessung: 260mm x 183mm x 41mm
• Gewicht: 1362g
• ISBN-13: 9780470531112
• ISBN-10: 0470531118
• Artikelnr.: 39373702

Autorenporträt

PAOLO BRANDIMARTE 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 Introduction to Distribution Logistics, Quantitative Methods: An Introduction for Business Management, and Numerical Methods in Finance and Economics: A MATLAB-Based Introduction, Second Edition, all published by Wiley.

Inhaltsangabe

Preface xiii Part I Overview and Motivation 1 Introduction to Monte Carlo
Methods 3 1.1 Historical origin of Monte Carlo simulation 4 1.2 Monte Carlo
Simulation vs. Monte Carlo Sampling 7 1.3 System dynamics and the mechanics
of Monte Carlo simulation 10 1.4 Simulation and optimization 21 1.5
Pitfalls in Monte Carlo simulation 30 1.6 Software tools for Monte Carlo
simulation 35 1.7 Prerequisites 37 For further reading 38 Chapter
References 38 2 Numerical Integration Methods 41 2.1 Classical quadrature
formulae 43 2.2 Gaussian quadrature 48 2.3 Extension to higher dimensions:
Product rules 53 2.4 Alternative approaches for high-dimensional
integration 55 2.5 Relationship with moment matching 67 2.6 Numerical
integration in R 69 For further reading 71 Chapter References 71 Part II
Input Analysis: Modeling and Estimation 3 Stochastic Modeling in Finance
and Economics 75 3.1 Introductory examples 77 3.2 Some common probability
distributions 86 3.3 Multivariate distributions: Covariance and correlation
111 3.4 Modeling dependence with copulae 127 3.5 Linear regression models:
a probabilistic view 136 3.6 Time series models 137 3.7 Stochastic
differential equations 158 3.8 Dimensionality reduction 177 S3.1
Risk-neutral derivative pricing 190 S3.1.1 Option pricing in the binomial
model 192 S3.1.2 A continuous-time model for option pricing: The
Black-Scholes-Merton formula 194 S3.1.3 Option pricing in incomplete
markets 199 For further reading 202 Chapter References 203 4 Estimation and
Fitting 205 4.1 Basic inferential statistics in R 207 4.2 Parameter
estimation 215 4.3 Checking the fit of hypothetical distributions 224 4.4
Estimation of linear regression models by ordinary least squares 229 4.5
Fitting time series models 232 4.6 Subjective probability: the Bayesian
view 235 For further reading 244 Chapter References 245 Part III Sampling
and Path Generation 5 Random Variate Generation 249 5.1 The structure of a
Monte Carlo simulation 250 5.2 Generating pseudo-random numbers 252 5.3 The
inverse transform method 263 5.4 The acceptance-rejection method 265 5.5
Generating normal variates 269 5.6 Other ad hoc methods 274 5.7 Sampling
from copulae 276 For further reading 277 Chapter References 279 6 Sample
Path Generation for Continuous-Time Models 281 6.1 Issues in path
generation 282 6.2 Simulating geometric Brownian motion 287 6.3 Sample
paths of short-term interest rates 298 6.4 Dealing with stochastic
volatility 306 6.5 Dealing with jumps 308 For further reading 310 Chapter
References 311 Part IV Output Analysis and Efficiency Improvement 7 Output
Analysis 315 7.1 Pitfalls in output analysis 317 7.2 Setting the number of
replications 323 7.3 A world beyond averages 325 7.4 Good and bad news 327
For further reading 327 Chapter References 328 8 Variance Reduction Methods
329 8.1 Antithetic sampling 330 8.2 Common random numbers 336 8.3 Control
variates 337 8.4 Conditional Monte Carlo 341 8.5 Stratified sampling 344
8.6 Importance sampling 350 For further reading 363 Chapter References 363
9 Low-Discrepancy Sequences 365 9.1 Low-discrepancy sequences 366 9.2
Halton sequences 367 9.3 Sobol low-discrepancy sequences 374 9.4 Randomized
and scrambled low-discrepancy sequences 379 9.5 Sample path generation with
low-discrepancy sequences 381 For further reading 385 Chapter References
385 Part V Miscellaneous Applications 10 Optimization 389 10.1
Classification of optimization problems 390 10.2 Optimization model
building 405 10.3 Monte Carlo methods for global optimization 412 10.4
Direct search and simulation-based optimization methods 416 10.5 Stochastic
programming models 420 10.6 Scenario generation and Monte Carlo methods for
stochastic programming 428 10.7 Stochastic dynamic programming 433 10.8
Numerical dynamic programming 440 10.9 Approximate dynamic programming 451
For further reading 453 Chapter References 453 11 Option Pricing 455 11.1
European-style multidimensional options in the BSM world 456 11.2
European-style path-dependent options in the BSM world 462 11.3 Pricing
options with early exercise features 475 11.4 A look outside the BSM world
487 11.5 Pricing interest-rate derivatives 490 For further reading 497
Chapter References 498 12 Sensitivity Estimation 501 12.1 Estimating option
greeks by finite differences 503 12.2 Estimating option greeks by pathwise
derivatives 509 12.3 Estimating option greeks by the likelihood ratio
method 513 For further reading 517 Chapter References 518 13 Risk
Measurement and Management 519 13.1 What is a risk measure? 520 13.2
Quantile-based risk measures: value at risk 522 13.3 Monte Carlo methods
for V@R 533 13.4 Mean-risk models in stochastic programming 537 13.5
Simulating delta-hedging strategies 540 13.6 The interplay of financial and
nonfinancial risks 546 For further reading 548 Chapter References 548 14
Markov Chain Monte Carlo and Bayesian Statistics 551 14.1 An introduction
to Markov chains 552 14.2 The Metropolis-Hastings algorithm 555 14.3 A
re-examination of simulated annealing 558 For further reading 560 Chapter
References 561 Index 563