numerical methods for pricing financial instruments
- ISBN: 9780750657228
- Editorial: Butterworth-Heinemann Ltd.
- Fecha de la edición: 2004
- Lugar de la edición: Oxford. Reino Unido
- Colección: Quantitative finance series
- Encuadernación: Cartoné
- Medidas: 24 cm
- Nº Pág.: 443
- Idiomas: Inglés
Computational Finance presents a modern computational approach to mathematical finance within the Windows environment, and contains financial algorithms, mathematical proofs and computer code in C/C++. The author illustrates how numeric components can be developed which allow financial routines to be easily called by the complete range of Windows applications, such as Excel, Borland Delphi, Visual Basic and Visual C++. 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. A CD-ROM is included which contains: working computer code, demonstration applications and also pdf versions of several research articles. ÍNDICE: 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.