Differentiating intraday seasonalities through wavelet multi-scaling

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

188 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Finance

View Paper: https://www.semanticscholar.org/paper/24b37e72cb11efea8b7025a3e5617f6da57b0d83

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

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Citations in the corpus

Using wavelets to decompose the time–frequency effects of monetary policy (2008, background): “[15, 16] is largely unknown to the majority of the economists, who reveal a strong preference for traditional econometric methods, overlooking the potential for using wavelets to analyze economic data.”
Dependence structures for multivariate high-frequency data in finance (2003, methodology): “Recently, a non-parametric, wavelet-based method has been proposed by Gençay et al (2001).”
THE CONTINUOUS WAVELET TRANSFORM: MOVING BEYOND UNI‐ AND BIVARIATE ANALYSIS (2014, unknown): “Then, from (33), we have 1 R(2) 1(3:::p) = Qd Q11 :”
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting (2008, background): “The wavelet transform decomposes a process into different scales, which makes it useful in differentiating seasonalities , revealing structural breaks and volatility clusters, and identifying local and global dynamic properties of a process at these timescales [5], [23], [45].”
The continuous wavelet transform : a primer (2011, unknown): “Withcher [21, 20, 22], Wong, Ip, Xie and Lui [45], Connor and Rossiter [9], Fernandez [15] and Gallegati and Gallegati [17].”
Wavelets in Economics and Finance: Past and Future (2002, methodology): “Non-symmetric filters introduce phase shifts, see for example, Percival and Walden (2000) and Gencay et al. (2002). Orthogonality is a very useful property of wavelets as it is of any transformation procedure, but does not hold universally for all wavelet classes. While orthogonality does not in the”
An empirical analysis of dynamic multiscale hedging using wavelet decomposition (2011, methodology): “…some of the difﬁculties associated with the DWT, in this paper we adopt the maximum overlap discrete wavelet transform (MODWT), a highly redundant linear ﬁlter that transforms a series into coefﬁcients related to variations over a set of scales, (Percival and Walden, 2000; Gencay et al. , 2001a).”
Systematic risk and timescales (2003, methodology): “Gençay et al (2001c) investigated the scaling properties of foreign exchange volatility through a multiscale decomposition of the variance and covariance between two time series on a scale-by-scale basis.”