Simulation Techniques in Financial Risk Management.

Cover -- Title Page -- Copyright -- Dedication -- Contents -- List of Figures -- List of Tables -- Preface -- Chapter 1 Preliminaries of VBA -- 1.1 Introduction -- 1.2 Basis Excel VBA -- 1.2.1 Developer Mode and Security Level -- 1.2.2 Visual Basic Editor -- 1.2.3 The Macro Recorder -- 1.2.4 Setting Up a Command Button -- 1.3 VBA Programming Fundamentals -- 1.3.1 Declaration of Variables -- 1.3.2 Types of Variables -- 1.3.3 Declaration of Multivariable -- 1.3.4 Declaration of Constants -- 1.3.5 Operators -- 1.3.6 User-Defined Data Types -- 1.3.7 Arrays and Matrices -- 1.3.8 Data Input and Output -- 1.3.9 Conditional Statements -- 1.3.10 Loops -- 1.3.11 Sub Procedures and Function Procedures -- 1.3.12 VBA's Built-In Functions -- Chapter 2 Basic Properties of Futures and Options -- 2.1 Introduction -- 2.1.1 Arbitrage and Hedging -- 2.1.2 Forward Contracts -- 2.1.3 Futures Contracts -- 2.2 Options -- 2.3 Exercises -- Chapter 3 Introduction to Simulation -- 3.1 Questions -- 3.2 Simulation -- 3.3 Examples -- 3.3.1 Quadrature -- 3.3.2 Monte Carlo -- 3.4 Stochastic Simulations -- 3.5 Exercises -- Chapter 4 Brownian Motions and Itô's Rule -- 4.1 Introduction -- 4.2 Wiener and Itô's Processes -- 4.3 Stock Price -- 4.4 Itô's Formula -- 4.5 Exercises -- Chapter 5 Black--Scholes Model and Option Pricing -- 5.1 Introduction -- 5.2 One Period Binomial Model -- 5.3 The Black--Scholes--Merton Equation -- 5.4 Black--Scholes Formula -- 5.5 Exercises -- Chapter 6 Generating Random Variables -- 6.1 Introduction -- 6.2 Random Numbers -- 6.3 Discrete Random Variables -- 6.4 Acceptance-Rejection Method -- 6.5 Continuous Random Variables -- 6.5.1 Inverse Transform -- 6.5.2 The Rejection Method -- 6.5.3 Multivariate Normal -- 6.6 Exercises -- Chapter 7 Standard Simulations in Risk Management -- 7.1 Introduction -- 7.2 Scenario Analysis -- 7.2.1 Value at Risk.

7.2.2 Heavy-Tailed Distribution -- 7.2.3 Case Study: VaR of Dow Jones -- 7.3 Standard Monte Carlo -- 7.3.1 Mean, Variance, and Interval Estimation -- 7.3.2 Simulating Option Prices -- 7.3.3 Simulating Option Delta -- 7.4 Exercises -- 7.5 Appendix -- Chapter 8 Variance Reduction Techniques -- 8.1 Introduction -- 8.2 Antithetic Variables -- 8.3 Stratified Sampling -- 8.4 Control Variates -- 8.5 Importance Sampling -- 8.6 Exercises -- Chapter 9 Path Dependent Options -- 9.1 Introduction -- 9.2 Barrier Option -- 9.3 Lookback Option -- 9.4 Asian Option -- 9.5 American Option -- 9.5.1 Simulation: Least Squares Approach -- 9.5.2 Analyzing the Least Squares Approach -- 9.5.3 American Style Path Dependent Options -- 9.6 Greek Letters -- 9.7 Exercises -- Chapter 10 Multiasset Options -- 10.1 Introduction -- 10.2 Simulating European Multiasset Options -- 10.3 Case Study: On Estimating Basket Options -- 10.4 Dimension Reduction -- 10.5 Exercises -- Chapter 11 Interest Rate Models -- 11.1 Introduction -- 11.2 Discount Factor and Bond Prices -- 11.3 Stochastic Interest Rate Models and Their Simulations -- 11.4 Hull--White Model -- 11.5 Fixed Income Derivatives Pricing -- 11.6 Exercises -- Chapter 12 Markov Chain Monte Carlo Methods -- 12.1 Introduction -- 12.2 Bayesian Inference -- 12.3 Simulating Posteriors -- 12.4 Markov Chain Monte Carlo -- 12.4.1 Gibbs Sampling -- 12.4.2 Case Study: The Effect of Jumps on Dow Jones -- 12.5 Metropolis--Hastings Algorithm -- 12.6 Exercises -- References -- Index -- Wiley Series in Statistics in Practice -- EULA.

Praise for the First Edition "…a nice, self-contained introduction to simulation and computational techniques in finance…"  - Mathematical Reviews Simulation Techniques in Financial Risk Management, Second Edition takes a unique approach to the field of simulations by focusing on techniques necessary in the fields of finance and risk management. Thoroughly updated, the new edition expands on several key topics in these areas and presents many of the recent innovations in simulations and risk management, such as advanced option pricing models beyond the Black-Scholes paradigm, interest rate models, MCMC methods including stochastic volatility models simulations, model assets and model-free properties, jump diffusion, and state space modeling. The Second Edition also features: Updates to primary software used throughout the book, Microsoft Office® Excel® VBA New topical coverage on multiple assets, model-free properties, and related models More than 300 exercises at the end of each chapter, with select answers in the appendix, to help readers apply new concepts and test their understanding Extensive use of examples to illustrate how to use simulation techniques in risk management Practical case studies, such as the pricing of exotic options; simulations of Greeks in hedging; and the use of Bayesian ideas to assess the impact of jumps, so readers can reproduce the results of the studies A related website with additional solutions to problems within the book as well as Excel VBA and S-Plus computer code for many of the examples within the book Simulation Techniques in Financial Risk Management, Second Edition is an invaluable resource for risk managers in the financial and actuarial industries as well as a useful reference for readers interested in learning how to better gauge risk and make more informed decisions. The book is also ideal

for upper-undergraduate and graduate-level courses in simulation and risk management.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.