Numerical Methods and Optimization in Finance

Description

Computationally-intensive tools play an increasingly important role in financial decisions. Many financial problems—ranging from asset allocation to risk management and from option pricing to model calibration—can be efficiently handled using modern computational techniques. Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics. This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested empirically. This revised edition includes two new chapters, a self-contained tutorial on implementing and using heuristics, and an explanation of software used for testing portfolio-selection models. Postgraduate students, researchers in programs on quantitative and computational finance, and practitioners in banks and other financial companies can benefit from this second edition of Numerical Methods and Optimization in Finance. Introduces numerical methods to readers with economics backgroundsEmphasizes core simulation and optimization problemsIncludes MATLAB and R code for all applications, with sample code in the text and freely available for download Contents List of figures List of tables List of algorithms Acknowledgments Foreword to the second edition Part I: Fundamentals 1. Introduction 2. Numerical analysis in anutshell 3. Linear equations and Least Squares problems 4. Finite difference methods 5. Binomial trees Part II: Simulation 6. Generating random numbers 7. Modeling dependencies 8. A gentle introduction to financial simulation 9. Financial simulation at work: some case studies Part III: Optimization 10. Optimization problems in finance 11. Basic methods 12. Heuristic methods in a nutshell 13. Heuristics: a tutorial 14. Portfolio optimization 15. Backtesting 16. Econometric models 17. Calibrating option pricing models Appendix A. The NMOF package Bibliography Index