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

Wavelets offer an opportunity to examine data or functions on a variety of scales. They also allow us to focus on local features of a data series in situations where traditional approaches, such as Fourier analysis, smear out some local detail. For instance, if we look at time series data we can use [...]
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Wavelets offer an opportunity to examine data or functions on a variety of scales. They also allow us to focus on local features of a data series in situations where traditional approaches, such as Fourier analysis, smear out some local detail. For instance, if we look at time series data we can use a chronological approach, which provides frequency content, or we can use a Fourier approach, which reveals frequency content but not time content. Wavelet analyses combine the two into a time frequency representation. The best analogy is in musical notation, which provides both the orderin which the notes should be played (time) and the pitch at which they should be played (frequency). Since the early 1980s, wavelets have been popular in engineering, geology, mathematics, meterorology, and signal processing. Economics, econometrics, and finance have been slower to apply wavelets to data and problems. *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