Numerical Methods and Optimization in Finance (2nd Ed.)
Auteurs : Gilli Manfred, Maringer Dietmar, Schumann Enrico
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.
1. Introduction
I. Fundamentals2. Numerical Analysis in a Nutshell3. Linear Equations and Least Squares Problems4. Finite Difference Methods5. Binomial Trees
II. Simulation6. Generating Random Numbers7. Modeling Dependencies8. A Gentle Introduction to Financial Simulation9. Financial Simulation at Work: Some Case Studies
III. Optimization10. Optimization Problems in Finance11. Basic Methods12. Heuristic Methods in a Nutshell13.: Heuristic Methods: A Tutorial14. Portfolio Optimization15. Backtesting Investment Strategies16. Econometric Models17. Calibrating Option Pricing Models
Students (Master or PhD level) and researchers in programs on quantitative and computational finance, and also practitioners in banks and other financial companies.
Dietmar Maringer is Professor of Computational Economics and Finance at the University of Basel, Switzerland, and a faculty member at the Geneva School of Economics and Management. His research interests include non-deterministic methods such as heuristic optimization and simulations, computational learning, and empirical methods, typically with applications in trading, risk, and financial management.
Enrico Schumann holds a Ph.D. in econometrics, an MSC in economics, and a BA in economics and law. He has written on numerical methods and their application in finance, with a focus on asset allocation. His research interests include quantitative investment strategies and portfolio construction, computationally-intensive methods (in particular, optimization), and automated data processing and analysis.
- Introduces numerical methods to readers with economics backgrounds
- Emphasizes core simulation and optimization problems
- Includes MATLAB and R code for all applications, with sample code in the text and freely available for download
Date de parution : 08-2019
Ouvrage de 638 p.
21.5x27.6 cm
Thèmes de Numerical Methods and Optimization in Finance :
Mots-clés :
Acceptance–rejection method; Asset selection; Backtesting; Backtesting software; Bootstrap; Box–Muller method; Brownian bridge; Constant proportion portfolio insurance (CPPI); Differential Evolution; Direct methods; Downside risk; Experimental design; Extreme value theory; Gap risk; GARCH; Gauss–Seidel method; Heston model; Industry momentum; Interest rate models; Inversion method; Iterative methods; Jacobi method; Least Median of Squares; Least Squares problems; Least Trimmed Squares; Local Search; Loglikelihood; Matrix factorization; Nelson–Siegel model; Nelson–Siegel–Svensson model; Optimization heuristics; Option pricing; Particle Swarm Optimization; Portfolio optimization; Pseudo-random numbers; Quadratic programming; Quasi-Monte Carlo; R; Random number generator; Risk–reward measures; Robust regression; SOR; Sparse matrices; Term structure models; Threshold Accepting; Time Series Models; Value-at-Risk