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

By Ramazan Gencay, F. Selçuk, Brandon Whitcher. 2001

1,148 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/808cfe1d5c1898d666907fac1ac5c26ec00ad0cc

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In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Analysis of cycle-to-cycle pressure oscillations in a diesel engine (2008, methodology): “Wavelets have been used for signal analysis in a wide variety of applications [3,12,21,33].”
International transmission of stock market movements: a wavelet analysis (2004, background): “Based on a unified view of wavelet filtering techniques that are potentially very useful in finance and economics, Gençay et al. (2001) presented several empirical examples using financial time series data.”
UNIT ROOT TESTS WITH WAVELETS (2010, methodology): “A detailed treatment of MODWT can be found in Percival and Walden (2000) and Gençay, Selçuk, and Whitcher (2001).”
Hockey sticks, principal components, and spurious significance (2005, methodology): “…we applied the algorithm hosking.sim from the waveslim package version 1.3 downloaded from www.cran.r-project. org/doc/packages/waveslim.pdf [Gencay et al., 2001], which applied a method due to Hosking [1984] to simulate trendless red noise based on the complete auto-correlation function.”
An improved wavelet–ARIMA approach for forecasting metal prices (2014, positive): “The results on the sensitivity of the MODWT-based approach to the wavelet function choice were consistent with those by Gencay et al. (2001).”
Crude oil price analysis and forecasting using wavelet decomposed ensemble model (2012, neutral): “[23].”
Long Cycles in Growth: Explorations Using New Frequency Domain Techniques with US Data (2010, neutral): “Both Gençay, Selçuk, and Whicher (2001) and Percival and Walden (2000) give a thorough and accessible description of the MODWT using matrix algebra.”
Efficiency and Cost of Economical Brain Functional Networks (2007, background): “For example, the wavelet coefficients of a long memory process are stationary, and wavelet-based estimators of the correlation between two long memory processes have been shown to have desirable statistical properties [18,19].”
Using wavelets to decompose the time–frequency effects of monetary policy (2008, neutral): “[17,39] or, more recently, Ref.”
Multi-period hedge ratios for a multi-asset portfolio when accounting for returns comovement (2008, methodology): “Examples of recent applications of copulas in finance are Cherubini and Luciano (2002, 2003a, b), Embrechts, Lindskog and McNeil (2003), Giesecke (2004), Junker, Szimayer, and Wagner (2006), Pachenko (2005), and Rosenberg and Schuermann (2006).”
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.”