An Introduction to Wavelets and Other Filtering Methods in Finance and Economics

Presents a unified view of filtering techniques, focused on wavelet analysis in finance and economics. Covering theory and methods, the work concentrates on what wavelets analysis and filtering methods in general can reveal about a time series, with examples and empirical applications.

Autorentext
Ramazan Gençay is a professor in the economics department at Simon Fraser University. His areas of specialization are financial econometrics, nonlinear time series, nonparametric econometrics, and chaotic dynamics. His publications appear in finance, economics, statistics and physics journals. His work has appeared in the Journal of the American Statistical Association, Journal of Econometrics, and Physics Letters A.Faruk Selçuk is a faculty member in the department of economics at Bilkent University, Ankara, Turkey. His research interests are time series analysis, financial econometrics, risk management, emerging market economies, and the Turkish economy. His recent publications appeared in Studies in Nonlinear Dynamics and Econometrics, International Journal of Forecasting, and Physica A. He is a consultant for Reuters-Istanbul and Reuters-Moscow.Brandon Whitcher is currently a visiting scientist in the Geophysical Statistics Project at the National Center for Atmospheric Research. He was a research scientist at EURANDOM, a European research institute for the study of stochastic phenomena, after receiving his Ph.D. in statistics from the University of Washington. His research interests include wavelet methodology, time series analysis, computational statistics, and applications in the physical sciences, finance, and economics. His publications have appeared in Exploration Geophysics, Journal of Computational and Graphical Statistics, Journal of Geophysical Research, Journal of Statistical Computation and Simulation, and Physica A.

Klappentext
An Introduction to Wavelets and Other Filtering Methods in Finance and Economics presents a unified view of filtering techniques with a special focus on wavelet analysis in finance and economics. It emphasizes the methods and explanations of the theory that underlies them. It also concentrates on exactly what wavelet analysis (and filtering methods in general) can reveal about a time series. It offers testing issues which can be performed with wavelets in conjunction with the multi-resolution analysis. The descriptive focus of the book avoids proofs and provides easy access to a wide spectrum of parametric and nonparametric filtering methods. Examples and empirical applications will show readers the capabilities, advantages, and disadvantages of each method.
The first book to present a unified view of filtering techniques
Concentrates on exactly what wavelets analysis and filtering methods in general can reveal about a time series
*Provides easy access to a wide spectrum of parametric and non-parametric filtering methods

Zusammenfassung
Presents a unified view of filtering techniques with a focus on wavelet analysis in finance and economics. This title emphasizes the methods and explanations of the theory that underlies them. It also concentrates on exactly what wavelet analysis (and filtering methods in general) can reveal about a time series.

Inhalt
Preface Introduction Linear Filters Optimum Linear Estimation Discrete Wavelet Transforms Wavelets and Stationary Processes Wavelet Denoising Wavelets for Variance-Covariance Estimation Artificial Neural Networks