Wavelets in Economics and Finance: Past and Future

By James B. Ramsey. 2002

441 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/0a4533f876be13981f103a30b3c2d1d34ee8660e

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Citations in the corpus

Analysis of Financial Time-Series Using Fourier and Wavelet Methods (2008, background): “Ramsey (2002) surveys the most important properties of wavelets and discusses their elds of application in both economics and nance.”
The continuous wavelet transform : a primer (2011, positive): “The Continuous Wavelet Transform: A Primer Maria Joana Soares1 Lúıs Aguiar-Conraria2 1Department of Mathematics and Applications/NIPE 2Department of Economics/NIPE University of Minho, Portugal Workshop on Frequency Domain Research in Macroeconomics and Finance Helsinki, 20−21 October 2011 Introduc”
Does bitcoin co-move and share risk with Sukuk and world and regional Islamic stock markets? Evidence using a time-frequency approach (2020, methodology): “The wavelet approach can handle nonstationary data, localization in time, and the resolution of the signal in terms of the time scale of analysis (Ramsey and Lampart, 1998; Ramsey, 2002).”
The co-movement between oil and clean energy stocks: A wavelet-based analysis of horizon associations (2019, methodology): “20 See the article by Ramsey (2002) for insights obtained from applying wavelets analysis to economic and financial data. 21 A DCC-GARCH of the wavelets inference is followed by many researchers: see for instance, Khalfaoui et al., (2012, 2015), Baruník et al. (2016) and Boubaker and Raza (2017).”
UNIT ROOT TESTS WITH WAVELETS (2010, background): “Early applications of wavelets in economics and finance are by Ramsey and his coauthors (for a review and references see Ramsey, Zaslavsky, and Usikov, 1995; Ramsey and Zhang, 1997; Ramsey, 1999, 2002), who explore the use of DWT in decomposing various economic and financial data.”
An empirical comparison of alternate regime-switching models or electricity spot prices (2010, background): “Secondly, while wavelets offer a very good in-sample fit to the data, their ability to forecast past the near future is rather bad (Ramsey, 2002).”
Using wavelets to decompose the time–frequency effects of monetary policy (2008, background): “[8]. 3 For detailed reviews of wavelet applications to economic and financial data, the reader is referred to Refs. [17, 39 ] or, more recently, Ref.”
An Intuitive Guide to Wavelets for Economists (2005, background): “This paper constitutes an extension to Schleicher (2002) and Ramsey (2002), given that many of the advances in wavelet analysis have only recently been made widely available on commercially available statistical software packages.”
Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions (2016, methodology): “The wavelet transform is able to capture all the information in the time series and associate it with specific time horizons and locations in time (Gençay et al., 2002).2 Following Ramsey (2002), it is possible to represent any function of time by the father (ϕ) and mother (ψ) wavelets.”
Gold, Oil, and Stocks: Dynamic Correlations (2016, positive): “Gençay, Selçuk, and Whitcher (2001) and Ramsey (2002) provide ample exposition on the use and versatility of wavelet techniques in economics and finance.”
Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis (2012, background): “As a result, wavelets have significant advantages over basic Fourier analysis when the object under study is locally stationary and inhomogeneous — see (Gençay et al., 2002; Percival and Walden, 2000; Ramsay, 2002).”