An introduction to wavelets and other filtering methods in finance and economics

By Ronnie Sircar. 2002

167 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

Jump detection with wavelets for high-frequency financial time series (2014, background): “If we want to estimate the integrated volatility i.e. disentangling the spot volatility of the underlying process (σt ) from the microstructure noise (η) is a challenging task, since σ ∗t is the only information available (See Zhang et al. (2005) and Zhang (2006)).”
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.”
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).”
An improved wavelet–ARIMA approach for forecasting metal prices (2014, unknown): “The results on the sensitivity of the MODWT-based approach to the wavelet function choice were consistent with those by Gencay et al. (2001).”
Hockey sticks, principal components, and spurious significance (2005, methodology): “Citation: McIntyre, S., and”
Analysis of Financial Time-Series Using Fourier and Wavelet Methods (2008, methodology): “Gençay et al. (2002) discuss the use of wavelets for speci c purposes in economics and nance and adopt a more intuitive approach (with many illustrations and examples).”
Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “MODWT, also called stationary wavelet transform (SWT), is an extended DWT representation using the “a trous” algorithm proposed by [17,18].”