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Sinopsis
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 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
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Acerca de los autores
Manfred Gilli is Professor emeritus at the Geneva School of Economics and Management at the University of Geneva, Switzerland, where he has taught numerical methods in economics and finance. He is also a Faculty member of the Swiss Finance Institute, a member of the Advisory Board of Computational Statistics and Data Analysis, and a member of the editorial board of Computational Economics. He formerly served as president of the Society for Computational Economics.
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.
De la contraportada
Numerical Methods and Optimization in Finance presents tools for computational finance with an emphasis on optimization techniques, specifically heuristics. Two new chapters in the Second Edition include a self-contained tutorial on using and implementing heuristics and an explanation of software used for testing portfolio-selection models. Every chapter has been updated and reviewed for accuracy. The authors concentrate on practical, computational applications of financial theory made into software form and tested empirically. For every topic, they discuss the implementation of methods. Algorithms are stated in pseudocode, and Matlab or R code is provided for all important examples.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Condición: New. KlappentextrnrnIntroduces 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 downloa. Nº de ref. del artículo: 282073610
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