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Full Description
This unique resource provides simulation techniques for financial risk managers ensuring you become well versed in many recent innovations, including Gibbs sampling, the use of heavy-tailed distributions in VaR calculations, construction of volatility smile, and state space modeling. The authors illustrate key concepts with examples and case studies you can reproduce using either S-PLUS(r) or Visual Basic(r) and provide exercises so you can apply new concepts and test your knowledge. Simulation Techniques in Financial Risk Management is invaluable both as a resource for risk managers in the financial and actuarial industries and as a coursebook for upper-level undergraduate and graduate courses in simulation and risk management.
Contents
List of Figures. List of Tables. Preface. 1. Introduction. 1.1 Questions. 1.2 Simulation. 1.3 Examples. 1.3.1 Quadrature. 1.3.2 Monte Carlo. 1.4 Stochastic Simulations. 1.5 Exercises. 2. Brownian Motions and Ito's Rule. 2.1 Introduction. 2.2 Wiener's and Ito's Processes. 2.3 Stock Price. 2.4 Ito's Formula. 2.5 Exercises. 3. Black-Scholes Model and Option Pricing . 3.1 Introduction. 3.2 One Period Binomial Model . 3.3 The Black-Scholes-Merton Equation . 3.4 Black-Scholes Formula. 3.5 Exercises. 4. Generating Random Variables. 4.1 Introduction. 4.2 Random Numbers. 4.3 Discrete Random Variables. 4.4 Acceptance-Rejection Method . 4.5 Continuous Random Variables. 4.5.1 Inverse Transform. 4.5.2 The Rejection Method. 4.5.3 Multivariate Normal. 4.6 Exercises. 5. Standard Simulations in Risk Management. 5.1 Introduction. 5.2 Scenario Analysis. 5.2.1 Value at Risk. 5.2.2 Heavy- Tailed Distribution. 5.2.3 Case Study: VaR of Dow Jones. 5.3 Standard Monte Carlo. 5.3.1 Mean, Variance, and Interval Estimation . 5.3.2 Simulating Option Prices. 5.3.3 Simulating Option Delta. 5.4 Exercises. 5.5 Appendix. 6. Variance Reduction Techniques. 6.1 Introduction. 6.2 Antithetic Variables. 6.3 Stratified Sampling 6.4 Control Variates. 6.5 Importance Sampling. 6.6 Exercises. 7. Path-Dependent Options. 7.1 Introduction. 7.2 Barrier Option. 7.3 Lookbaclc Option. 7.4 Asian Option. 7.5 American Option. 7.5.1 Simulation: Least Squares Approach. 7.5.2 Analyzing the Least Squares Approach. 7.5.3 American-Style Path-Dependent Options. 7.6 Greek Letters. 7.7 Exercises. 8. Multi-asset Options. 8.1 Introduction. 8.2 Simulating European Multi-Asset Options. 8.3 Case Study: On Estimating Basket Options. 8.4 Dimensional Reduction. 8.5 Exercises. 9. Interest Rate Models. 9.1 Introduction. 9.2 Discount Factor. 9.2.1 Time- Varying Interest Rate. 9.3 Stochastic Interest Rate Models and Their Simulations. 9.4 Options with Stochastic Interest Rate. 9.5 Exercises. 10. Markov Chain Monte Carlo Methods. 10.1 Introduction. 10.2 Bayesian Inference. 10.3 Simulating Posteriors. 10.4 Marlcov Chain Monte Carlo. 10.4.1 Gibbs Sampling. 10.4.2 Case Study: The Impact of Jumps on Dow Jones. 10.5 Metropolis- Hustings Algorithm. 10.6 Exercises. 11. Answers to Selected Exercises. 11.1 Chapter 1. 11.2 Chapter 2. 11.3 Chapter 3. 11.4 Chapter 4. 11.5 Chapter 5. 11.6 Chapter 6. 11.7 Chapter 7. 11.8 Chapter 8. 11.9 Chapter 9. 11.10 Chapter 10. References. Index.
