The contribution of wavelets to the analysis of economic and financial data

Ramsey, James B.

doi:10.1098/rsta.1999.0450

Cited by 191 publications

(103 citation statements)

References 23 publications

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“…The MODWT is used to compute wavelet variances, wavelet correlations (WC) and cross-correlations (WCC) of bivariate time series [13,14]. Several applications of the DWT and MODWT can be found in economic and financial literature (see e.g., [8,13,23,24]) and to a lesser extent in energy studies [25][26][27]. Nevertheless, to date the WCC (via MODWT) has not been widely used to study the correlation and co-movements between spot and long-term futures oil prices (though some exceptions are discussed below).…”

Section: The Wavelet Methodologymentioning

confidence: 99%

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Polanco-Martínez

Abadie

2016

Energies

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Abstract:The West Texas Intermediate (WTI) spot price shows high volatility and in 2014 and 2015 when quoted prices declined sharply, long-term prices in future markets were less volatile. These prices are different and diverge depending on how they process fundamental and transitory factors. US tight oil production has been a major innovation with significant macroeconomic effects. In this paper we use WTI spot prices and long-term futures prices, the latter calculated as the expected value with a stochastic model calibrated with the futures quotes of each sample day. These long-term prices are the long-term equilibrium value under risk neutral measurement. In order to analyze potential time-scale relationships between spots and future, we perform a wavelet cross-correlation analysis using a novel wavelet graphical tool recently proposed. To check the direction of the causality, we apply non-linear causality tests to raw data and log returns as well as to the wavelet transform of the spot and futures prices. Our results show that in the spot and futures markets for the period 24 February 2006-2 April 2016 there is a bi-directional causality effect for most time scales (from intra-week to biannual). This suggests that spot and futures prices react simultaneously to new information.

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Section: The Wavelet Methodologymentioning

confidence: 99%

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Polanco-Martínez

Abadie

2016

Energies

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“…Let us recall finally that other works previously addressed the wavelet techniques for stock market data: see the review paper [24] and the book [10]. Concerning this book, the authors works primarily with the discontinuous Haar wavelet which in general forbids to use any of these scale-limited extrapolation techniques.…”

Section: Preprint Submitted To Elsevier Sciencementioning

confidence: 99%

Analysis and short-time extrapolation of stock market indexes through projection onto discrete wavelet subspaces

Gosse¹

2010

Nonlinear Analysis: Real World Applications

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To cite this version:Laurent Gosse. Analysis and short-time extrapolation of stock market indexes through projection onto discrete wavelet subspaces. Nonlinear Analysis Real World Applications, 2010Applications, , 11, pp.3139-3154. <10.1016Applications, /j.nonrwa.2009 Analysis and short-time extrapolation of stock market indexes through projection onto discrete wavelet subspaces Laurent GosseIAC-CNR "Mauro Picone" (sezione di Bari) Via Amendola 122/I -70126 Bari, Italy AbstractWe consider the problem of short-time extrapolation of blue chips' stocks indexes in the context of wavelet subspaces following the theory proposed by X.-G. Xia and co-workers in a series of papers [29,28,14,15]. The idea is first to approximate the oscillations of the corresponding stock index at some scale by means of the scaling function which is part of a given multi-resolution analysis of L 2 (R). Then, since oscillations at a finer scale are discarded, it becomes possible to extend such a signal up to a certain time in the future; the finer the approximation, the shorter this extrapolation interval. At the numerical level, a so-called Generalized GerchbergPapoulis (GGP) algorithm is set up which is shown to converge toward the minimum L 2 norm solution of the extrapolation problem. When it comes to implementation, an acceleration by means of a Conjugate Gradient (CG) routine is necessary in order to obtain quickly a satisfying accuracy. Several examples are investigated with different international stock market indexes.

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“…Wavelet theory is applied for data processing, since the representation of wavelet can deal with the non-stationary involved in the economic and financial time series [11]. Wavelet is a mathematical function that has certain properties that oscillate around zero (like sine and cosine functions) and localized in the time domain, this mean when the domain value is relatively large, the wavelet function will be zero [10].…”

Section: Wavelet Decompositionmentioning

confidence: 99%

Application of Wavelet Neuro-Fuzzy System (WNFS) method for stock forecasting

Artha

Yasin

Warsito

et al. 2018

J. Phys.: Conf. Ser.

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Forecasting is a very important element in decision making, because the effectiveness of a decision, generally depends on several factors that we cannot see when the decision was taken. In this study, the Wavelet Neuro-Fuzzy System (WNFS) model that combines wavelet transformation and neuro-fuzzy techniques is applied to forecast daily closing stock price data of BMRI.JK. The observed daily stock price data are decomposed into some sub-series components by maximal overlap discrete wavelet transform (MODWT), then the appropriate sub-series that have higher correlation to the real data are used as inputs of the neuro-fuzzy model for daily forecasting stock price for three days in advance. The neuro-fuzzy model is begun with determining the membership value of each data using Fuzzy C-Means, followed by fuzzy inference procedure of the Sugeno model. The result shows that the presence of wavelet input in Neuro-Fuzzy System, can provide optimal prediction in daily stock price data, with small error value of predicted result. This would be helped investors or economists to produce meaningful information in either buy or sell a stock.

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The contribution of wavelets to the analysis of economic and financial data

Cited by 191 publications

References 23 publications

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Analysis and short-time extrapolation of stock market indexes through projection onto discrete wavelet subspaces

Application of Wavelet Neuro-Fuzzy System (WNFS) method for stock forecasting

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