Statistical Methods for Stochastic Differential Equations

1. Auflage 2012
ISBN: 978-1-4398-4940-8
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 508 Seiten, Format (B × H): 155 mm x 234 mm, Gewicht: 839 g

Reihe: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations. Written to be accessible to both new students and seasoned researchers, each self-contained chapter starts with introductions to the topic at hand and builds gradually towards discussing recent research.

The book covers Wiener-driven equations as well as stochastic differential equations with jumps, including continuous-time ARMA processes and COGARCH processes. It presents a spectrum of estimation methods, including nonparametric estimation as well as parametric estimation based on likelihood methods, estimating functions, and simulation techniques. Two chapters are devoted to high-frequency data. Multivariate models are also considered, including partially observed systems, asynchronous sampling, tests for simultaneous jumps, and multiscale diffusions.

Statistical Methods for Stochastic Differential Equations is useful to the theoretical statistician and the probabilist who works in or intends to work in the field, as well as to the applied statistician or financial econometrician who needs the methods to analyze biological or financial time series.

Zielgruppe

Researchers and graduate students in statistics, mathematics, finance, and econometrics.

Autoren/Hrsg.

Kessler, Mathieu

Lindner, Alexander

Sorensen, Michael

Fachgebiete

• Mathematik | Informatik Mathematik Mathematische Analysis Differentialrechnungen und -gleichungen
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Estimating functions for diffusion-type processes. The econometrics of high frequency data. Statistics and high frequency data. Importance sampling techniques for estimation of diffusion models. Non parametric estimation of the coefficients of ergodic diffusion processes based on high frequency data. Ornstein–Uhlenbeck related models driven by Lévy processes. Parameter estimation for multiscale diffusions: an overview.

Über Autor(innen)

Matthieu Kessler, Department of Applied Mathematics and Statistics, University of Cartagena, Spain

Alexander Lindner, Institute of Mathematics and Statistics, TU Braunschweig, Germany

Michael Sorensen, Department of Mathematical Sciences, University of Copenhagen, Denmark