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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.
Liquid markets generate hundreds or thousands of ticks (the minimum
change in price a security can have, either up or down) every
business day. Data vendors such as Reuters transmit more than
275,000 prices per day for foreign exchange spot rates alone. Thus,
high-frequency data can be a fundamental object of study, as
traders make decisions by observing high-frequency or tick-by-tick
data. Yet most studies published in financial literature deal with
low frequency, regularly spaced data. For a variety of reasons,
high-frequency data are becoming a way for understanding market
microstructure. This book discusses the best mathematical models
and tools for dealing with such vast amounts of data.
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