Carmona Statistical Analysis of Financial Data in S-Plus

Erscheinungsjahr 2006
ISBN: 978-0-387-21824-3
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 455 Seiten, eBook

Reihe: Springer Texts in Statistics

This book develops the use of statistical data analysis in finance, and it uses the statistical software environment of S-PLUS as a vehicle for presenting practical implementations from financial engineering. It is divided into three parts. Part I, Exploratory Data Analysis, reviews the most commonly used methods of statistical data exploration. Its originality lies in the introduction of tools for the estimation and simulation of heavy tail distributions and copulas, the computation of measures of risk, and the principal component analysis of yield curves. Part II, Regression, introduces modern regression concepts with an emphasis on robustness and non-parametric techniques. The applications include the term structure of interest rates, the construction of commodity forward curves, and nonparametric alternatives to the Black Scholes option pricing paradigm. Part III, Time Series and State Space Models, is concerned with theories of time series and of state space models. Linear ARIMA models are applied to the analysis of weather derivatives, Kalman filtering is applied to public company earnings prediction, and nonlinear GARCH models and nonlinear filtering are applied to stochastic volatility models. The book is aimed at undergraduate students in financial engineering, master students in finance and MBA's, and to practitioners with financial data analysis concerns.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Carmona, René

Inhaltsverzeichnis

Data Exploration, Estimation and Simulation.- Univariate Exploratory Data Analysis.- Multivariate Data Exploration.- Regression.- Parametric Regression.- Local & Nonparametric Regression.- Time Series & State Space Models.- Time Series Models: AR, MA, ARMA, & All That.- Multivariate Time Series, Linear Systems and Kalman Filtering.- Nonlinear Time Series: Models and Simulation.