Handbook in Monte Carlo simulation : applications in financial engineering, risk management, and economics / Paolo Brandimarte.

Includes bibliographical references and index.

Print version record and CIP data provided by publisher.

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.

Half Title page; Title page; Copyright page; Preface; Part One: Overview and Motivation; Chapter One: Introduction to Monte Carlo Methods; 1.1 Historical origin of Monte Carlo simulation; 1.2 Monte Carlo simulation vs. Monte Carlo sampling; 1.3 System dynamics and the mechanics of Monte Carlo simulation; 1.4 Simulation and optimization; 1.5 Pitfalls in Monte Carlo simulation; 1.6 Software tools for Monte Carlo simulation; 1.7 Prerequisites; For further reading; References; Chapter Two: Numerical Integration Methods; 2.1 Classical quadrature formulas; 2.2 Gaussian quadrature.

2.3 Extension to higher dimensions: Product rules2.4 Alternative approaches for high-dimensional integration; 2.5 Relationship with moment matching; 2.6 Numerical integration in R; For further reading; References; Part Two: Input Analysis: Modeling and Estimation; Chapter Three: Stochastic Modeling in Finance and Economics; 3.1 Introductory examples; 3.2 Some common probability distributions; 3.3 Multivariate distributions: Covariance and correlation; 3.4 Modeling dependence with copulas; 3.5 Linear regression models: A probabilistic view; 3.6 Time series models.

3.7 Stochastic differential equations3.8 Dimensionality reduction; 3.9 Risk-neutral derivative pricing; For further reading; References; Chapter Four: Estimation and Fitting; 4.1 Basic inferential statistics in R; 4.2 Parameter estimation; 4.3 Checking the fit of hypothetical distributions; 4.4 Estimation of linear regression models by ordinary least squares; 4.5 Fitting time series models; 4.6 Subjective probability: The Bayesian view; For further reading; References; Part Three: Sampling and Path Generation; Chapter Five: Random Variate Generation.

5.1 The structure of a Monte Carlo simulation5.2 Generating pseudorandom numbers; 5.3 The inverse transform method; 5.4 The acceptance-rejection method; 5.5 Generating normal variates; 5.6 Other ad hoc methods; 5.7 Sampling from copulas; For further reading; References; Chapter Six: Sample Path Generation for Continuous-Time Models; 6.1 Issues in path generation; 6.2 Simulating geometric Brownian motion; 6.3 Sample paths of short-term interest rates; 6.4 Dealing with stochastic volatility; 6.5 Dealing with jumps; For further reading; References.

Part Four: Output Analysis and Efficiency ImprovementChapter Seven: Output Analysis; 7.1 Pitfalls in output analysis; 7.2 Setting the number of replications; 7.3 A world beyond averages; 7.4 Good and bad news; For further reading; References; Chapter Eight: Variance Reduction Methods; 8.1 Antithetic sampling; 8.2 Common random numbers; 8.3 Control variates; 8.4 Conditional Monte Carlo; 8.5 Stratified sampling; 8.6 Importance sampling; For further reading; References; Chapter Nine: Low-Discrepancy Sequences; 9.1 Low-discrepancy sequences; 9.2 Halton sequences.