Book description
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.
Table of contents
- Cover
- 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
-
Chapter One: Introduction to Monte Carlo Methods
-
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 equations
- 3.8 Dimensionality reduction
- 3.9 Risk-neutral derivative pricing
- For further reading
- References
- Chapter Four: Estimation and Fitting
-
Chapter Three: Stochastic Modeling in Finance and Economics
- Part Three: Sampling and Path Generation
- Part Four: Output Analysis and Efficiency Improvement
-
Part Five: Miscellaneous Applications
-
Chapter Ten: Optimization
- 10.1 Classification of optimization problems
- 10.2 Optimization model building
- 10.3 Monte Carlo methods for global optimization
- 10.4 Direct search and simulation-based optimization methods
- 10.5 Stochastic programming models
- 10.6 Stochastic dynamic programming
- 10.7 Numerical dynamic programming
- 10.8 Approximate dynamic programming
- For further reading
- References
-
Chapter Eleven: Option Pricing
- 11.1 European-style multidimensional options in the BSM world
- 11.2 European-style path-dependent options in the BSM world
- 11.3 Pricing options with early exercise features
- 11.4 A look outside the BSM world: Equity options under the Heston model
- 11.5 Pricing interest rate derivatives
- For further reading
- References
- Chapter Twelve: Sensitivity Estimation
-
Chapter Thirteen: Risk Measurement and Management
- 13.1 What is a risk measure?
- 13.2 Quantile-based risk measures: Value-at-risk
- 13.3 Issues in Monte Carlo estimation of V@R
- 13.4 Variance reduction methods for V@R
- 13.5 Mean–risk models in stochastic programming
- 13.6 Simulating delta hedging strategies
- 13.7 The interplay of financial and nonfinancial risks
- For further reading
- References
- Chapter Fourteen: Markov Chain Monte Carlo and Bayesian Statistics
-
Chapter Ten: Optimization
- Index
Product information
- Title: Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics
- Author(s):
- Release date: May 2014
- Publisher(s): Wiley
- ISBN: 9780470531112
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