Buch, Englisch, 460 Seiten, Format (B × H): 165 mm x 234 mm, Gewicht: 910 g
Numerical Methods for Pricing Financial Instruments
Buch, Englisch, 460 Seiten, Format (B × H): 165 mm x 234 mm, Gewicht: 910 g
ISBN: 978-0-7506-5722-8
Verlag: Elsevier Science & Technology
These components permit software developers to call mathematical finance functions more easily than in corresponding packages. Although these packages may offer the advantage of interactive interfaces, it is not easy or computationally efficient to call them programmatically as a component of a larger system. The components are therefore well suited to software developers who want to include finance routines into a new application.
Typical readers are expected to have a knowledge of calculus, differential equations, statistics, Microsoft Excel, Visual Basic, C++ and HTML.
Zielgruppe
Financial Analysts; Financial Engineers; Numerical Analysts; Investment Portfolio Managers; MATLAB Users in Investment Banking, Commercial Banking, Insurance, and Corporate Finance; MSc courses in Computational Finance
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Unternehmensfinanzierung
- Wirtschaftswissenschaften Betriebswirtschaft Unternehmensfinanzen Finanzierung, Investition, Leasing
- Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware Buchhaltungssoftware
- Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
Weitere Infos & Material
Using Numerical Software Components with Microsoft Windows: Introduction; Dynamic Link Libraries (DLLs); ActiveX and COM; A financial derivative pricing example; ActiveX components and numerical optimization; XML and transformation using XSL; Epilogue; Pricing Assets: Introduction; Analytical methods and single asset European options; Numeric methods and single asset American options; Monte Carlo simulation; Multiasset European and American options; Dealing with missing data; Financial Econometrics: Introduction; GARCH models; Nonlinear GARCH; GARCH conditional probability distributions; Maximum likelihood parameter estimation; Analytic derivatives of the log likelihood; GJR-GARCH algorithms; GARCH software; GARCH process identification; Multivariate time series; Appendices.