Continuous Wavelet Decomposition Research Articles

Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications

PurposeThe main objective of this paper is to analyze the dynamic volatility spillovers between the investor's behavioral biases, the macroeconomic instability factors and the value at risk of the US Fintech stock market before and during the COVID-19 pandemic.Design/methodology/approachThe authors used the methodologies proposed by Diebold and Yilmaz (2012) and the wavelet approach.FindingsThe wavelet coherence results show that during the COVID-19 period, there was a strong co-movement among value at risk and each selected variables in the medium-run and the long-run scales. Diebold and Yilmaz's (2012) method proved that the total connectedness index raised significantly during the COVID-19 period. Moreover, the overconfidence bias and the financial stress index are the net transmitters, while the value at risk and herding behavior variables are the net receivers.Research limitations/implicationsThis study offers some important implications for investors and policymakers to explain the impact of the COVID-19 pandemic on the risk of Fintech industry.Practical implicationsThe study findings might be useful for investors to better understand the time–frequency connectedness and the volatility spillover effects in the context of COVID-19 pandemic. Future research may deal with investors' ability of constructing portfolios with another alternative index like cryptocurrencies which seems to be a safer investment.Originality/valueTo the best of the authors' knowledge, this is the first study that relies on the continuous wavelet decomposition technique and spillover volatility to examine the connectedness between investor behavioral biases, uncertainty factors, and Value at Risk of US Fintech stock markets, while taking into account the recent COVID-19 pandemic.

Revisiting the Energy-Growth nexus with debt channel. A wavelet time-frequency analysis for a panel of Eurozone-OECD countries

<abstract> <p>In this paper, continuous wavelet decompositions regarding the notions of coherence and phase are used to analyze the time-frequency dynamics of the existing relationships between energy supply and economic growth for a group of European countries. The objective is to identify both the intensity and the direction of the relationship over time and across frequencies. We also study the existence of a debt channel implying an indirect relationship between energy and growth. Our results show the complexity of the energy supply-growth relationship composed by direct effect at the short run and indirect effects through debt channel at the mid and long run. The countries with the highest debt/GDP ratio are more subject to such direct and indirect effects than others where only short run direct effect is noted.</p> </abstract>

Specific Recognition Technology of Infrared Absorption Spectra Based on Continuous Wavelet Decomposition

Because infrared (IR) absorption spectroscopy technology can offer high sensitivity and strong anti-interference capabilities, it is widely used in gas detection. To solve the problem of spectrum line aliasing in gas detection, this study examined the application of IR absorption spectroscopy technology based on time-frequency analysis in component identification. The second derivative spectrum of the IR absorption spectroscopy was processed by continuous wavelet transform to obtain the time-frequency characteristic matrix. The appropriate scale range was selected through the variance of wavelet coefficients. The correlation analysis of time and frequency on the time-frequency characteristic matrix was used for component identification. The experimental results showed that the correlation analysis of the time dimension can extract the characteristic absorption position of the gas to be measured in the gas mixture. The frequency correlation analysis at the characteristic absorption position can improve the recognition accuracy compared with the frequency correlation analysis in the entire spectral interval. The research in this article provides new ideas for the quantitative detection of gases.

Inversion of Soil Iron Oxide Based on Multi-Scale Continuous Wavelet Decomposition

Inversion of Soil Iron Oxide Based on Multi-Scale Continuous Wavelet Decomposition

Exchange Rate Interdependence in ASEAN Markets: A Wavelet Analysis

Severe swings among the financial markets’ linkages in the recent past motivate the research to examine the exchange rate co-movements. Therefore, this study examines the interdependence and contagion among the exchange rates of five important Asian emerging markets, namely Indonesia, Malaysia, Philippines, Singapore and Thailand exploring the multifarious nature of interdependence and lead lag association using discrete and continuous wavelet decomposition. The findings show the co-movement among many exchange rate pairs showing a substantial increase during the global financial crisis. There are moderate rises in co-movements, indicating regional market convergence in the long run. Singapore seems to hold driving position and Indonesia falls behind in all exchange rate pairs of the ASEAN-5. The findings provide policy implications for caring the interdependence in foreign exchange markets.

Multiscale analyses of pavement texture during polishing

Skid resistance is essential for road user safety and depends directly on the texture of the pavement. This texture is related to the surface texture of the aggregates. During its lifetime, the texture of the pavement evolves under the wear induced by road traffic. This phenomenon is called polishing. This paper presents a multiscale method based on the continuous wavelet decomposition of pavement texture to follow the evolution of the texture of aggregates during polishing at different scales. Several indicators are proposed to assess texture evolution and to correlate with friction evolution. The relevance of these parameters is assessed on samples composed of aggregates, which surface is characterized through laboratory experiments.

Generalized Localization Principle for Continuous Wavelet Decompositions

Spherically symmetric continuous wavelet decompositions are considered, and the notion of Riesz means is introduced for them. Generalized localization is proved for the decompositions under study in Lp classes without any restrictions on the wavelets. Further, generalized localization is studied for the Riesz means of wavelet decompositions of distributions from the Sobolev class with negative order of smoothness.

Transmission of shocks between Chinese financial market and oil market

PurposeThe purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period.Design/methodology/approachThis study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model.FindingsThe wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets.Originality/valueThis study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

Generalized Localization Principle for Continuous Wavelet Decompositions

В работе рассматриваются сферически симметричные непрерывные всплеск-разложения и вводится для них понятие средних Рисса. В классах $L_p$ без каких-либо ограничений на всплески, доказана справедливость обобщенной локализации для рассматриваемых разложений. Далее обобщенная локализация изучена для средних Рисса всплеск-разложений распределений из соболевского класса с отрицательным порядком гладкости. Библиография: 12 названий.

Multi-scale and multi-fractal analysis of abrasive wear signature of honing process

Honing is an industrial alternative of internal grinding for finishing large-diameter bores of internal combustion engine cylinders. This paper introduces a hybrid approach for a multi-scale analysis of surface finish in abrasive honing. The methodology is based on morphological identification of scratch pattern of manufactured surfaces, produced at various honing conditions, and their 2D continuous wavelet decomposition. The activated abrasion mechanisms were then discussed based on quantitative analysis of geometrical scratch pattern and multi-scale surface modifications of honing signature. Results show clearly that honed surface finish is primarily controlled by the size and the geometry of abrasive grains (i.e. wear of abrasive sticks). Since the abrasive is in continuous balanced contact with the work in honing operating at constant-force grinding, there is less deflection of the work. Moreover, it appears that honing signature shows a multi-scale fractal structure along the observation scales. This makes it possible to identify an optimal honing route preserving honing sticks abrasiveness and increasing their texturing capacity to confer high volume of oil retention at honed surface finish.

Contagion and interdependence across Asia-Pacific equity markets: An analysis based on multi-horizon discrete and continuous wavelet transformations

Our study attempts to discover contagion amongst the Asia-Pacific equity markets (Japan, Hong Kong and Australia) during twelve major crises around the world. We apply both discrete and continuous wavelet decompositions to unveil the multi-horizon nature of co-movement and lead–lag relationship. We find that shocks were transmitted via excessive linkages, with the Asian crisis as the most influential in relation to a sudden stop. We also find that the subprime crisis revealed fundamentals-based contagion, due to the strengthening fundamental linkages, with a dominant role of the Japanese market. Finally, we find low co-movements in the short run, suggesting a partial convergence across the markets.

Continuous Wavelet Decomposition Algorithms for Automatic Detection of Compressional‐ and Shear‐Wave Arrival Times

Abstract The determination of P ‐ and S ‐wave arrival times is important for a variety of seismological applications including earthquake detection and seismic tomography. The method is based on the continuous wavelet transform of the waveforms. Unlike Fourier transform, the basis functions are localized in time and frequency, therefore, wavelet transform is suitable for analysis of nonstationary signals. For detecting the P ‐wave arrival, the wavelet coefficients are calculated using the vertical component of the seismogram. In the case of the S ‐wave arrival, we take advantage of the polarization of the shear waves, and cross examine the wavelet coefficients from two horizontal recordings. In addition, shear‐wave splitting, the time delay of polarized S waves, can be measured using real and imaginary wavelets. Because these steps can be automated, application of the technique can easily generate a large database of shear‐wave splitting measurements for studies of anisotropy.

Characterization of oil–water two-phase pipe flow with a combined conductivity/capacitance sensor and wavelet analysis

Flow patterns of horizontal oil–water two-phase pipe flow were studied with water holdup fluctuations provided by a set of conductivity and capacitance sensors. The in situ water fraction measured by the combined sensor was calibrated by quick-closing valves. Local wavelet energy (LWE) coefficients were extracted through continuous wavelet decomposition of the phase fraction history. The flow patterns in the horizontal pipe were identified visually and classified as water-continuous flow (oil dispersed in water flow, stratified flow with mixing at the interface, dispersion of oil in water and water flow and dispersion of water in oil, and oil in water flow) and oil-continuous flow (oil and dispersion of oil in water flow, and water dispersed in oil flow). The mechanics of flow behaviors of each flow pattern were interpreted from the LWE coefficient maps. Two features, the normalized LWE coefficient W⁎ and the normalized scale a⁎, were extracted from the LWE coefficient maps to represent changes in flow patterns. Using these two coefficients, the flow pattern transition with superficial flow velocity of water (Jw) and oil (Jo) was investigated. An overall flow pattern transition is characterized in a W⁎−a⁎ map, where a “triangular” distribution of flow patterns is formed and the flow pattern transition can be characterized with the change in phase fraction and overall flow velocity.

NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI

Near-infrared spectroscopy (NIRS) is a non-invasive optical technique used for brain–computer interface (BCI). This study aims to investigate the brain hemodynamic responses of clench force and speed motor imagery and extract task-relevant features to obtain better classification performance. Given the non-stationary characteristics of real hemodynamic measurements, empirical mode decomposition (EMD) was applied to reduce the physiological noise overwhelmed in the task-relevant NIRS signals. Compared with continuous wavelet decomposition, EMD does not require a pre-determined basis function. EMD decomposes the original signals into a set of intrinsic mode functions (IMFs). In this study, joint mutual information was applied to select the optimal features, and support vector machine was used as a classifier. Offline and pseudo-online analyses showed that the most feasible classification accuracy can be obtained using IMFs as input features. Accordingly, an alternative feature is provided to develop the NIRS–BCI system.

Dependence of tooth flank finishing on powertrain gear noise

Finishing processes can be used in order to reduce manufacturing errors on teeth flank surfaces. These surfaces are at the heart of the gear meshing mechanics and thus should have an impact on gear noise. This paper addresses the issue of the impact tooth flank finishing on noise and vibration. The topographic and vibratory performances of two finishing processes, grinding and powerhoning, were compared. Multiscale analysis through continuous wavelet decomposition was used in order to characterize the teeth surfaces as well as the vibration spectra of a powertrain transmission tested on an industrial bench in a wide range of rotational speeds and frequencies. Results show that flank finishing processes leave their signature on the teeth surfaces which impact the vibratory behavior of the gear. Moreover, multiscale analysis approach allows the separation of vibration sources (meshing, environment, etc), and permits to quantify the impact of the choice of the finishing process.

Analysis of pressure fluctuations induced by multi‐horizontal submerged jets in the novel jet tank

AbstractBy means of detrending moving average and rescaled range analysis, the multifractal characteristics of the pressure fluctuation rate series (PFRS) derived from instantaneous fluctuations signals (PFS) in circulating jet tank (CJT) were investigated and the criteria of different scales were established. The central parts of the probability density functions of PFRS were exponentially distributed, and the positive part decayed slower than the negative part. The existence of multifractal characteristics of PFRS under different circumferential angles had been confirmed by both the strong q‐dependence of generalised Hurst exponents and continuous wavelet decompositions. The high frequency Hurst exponents based on rescaled range analysis were all >0.66 with the changing Re, which presented that PFRS had the long‐range dependence and aperiodicity. The local fractal dimensions declined with the increasing scales and presented the same trend at different dimensionless radial positions r/R0. The decomposed fluctuation components at scale a = 1–4 had different local fractal dimensions which revealed that the fluid flow was more complicated. The fluctuation energy profiles of PFRS indicated that the instantaneous pressure fluctuations were dominated by the interaction between macro‐scale vortexes and liquid phase.

Using Orthogonal Polynomial Trend Analysis and Wavelet decomposition (WOPTA) to investigate learning in a Mental Rotation task

Event Abstract Back to Event Using Orthogonal Polynomial Trend Analysis and Wavelet decomposition (WOPTA) to investigate learning in a Mental Rotation task Alexander Provost1, 2, 3*, Bryan Paton4, 5, 6, 7, Frini Karayanidis2, 3, Scott Brown1, 3 and Andrew Heathcote1, 3 1 the University of Newcastle, Australia 2 the University of Newcastle, Australia 3 Hunter Medical Research Institute, Australia 4 Monash University, Australia 5 Monash University, School of Psychology & Psychiatry, Australia 6 Monash University, Australia 7 Monash University, School of Psychology & Psychiatry, Australia BACKGROUND The orthogonal polynomial regression technique (OPTA) has been used previously in paradigms with small trial numbers to investigate learning effects in a habituation ERP paradigm. This analysis approach can dramatically improve SNR. The inclusion of a covariate as part of this regression analysis facilitates investigation of systematic changes in ERP morphology. Traditional ERP analysis methods are blind to the systematic and often dynamic changes that occur during practice or fatigue or in relation to a covariate of interest. Karayanidis et al (2011) used this approach successfully to investigate variability of cue-locked ERP components associated with advance preparation as a function of response time, by applying OPTA in the frequency domain. We are proposing an extension to the OPTA technique, using the continuous wavelet decomposition that will take advantage of the richer time-frequency structure of the data. We are using a mental rotation paradigm designed to encourage transition from mental rotation mastery to direct memory access, using OPTA to investigate the transition between these two strategies. METHODS We compared a wavelet-based OPTA (WOPTA) and a standard OPTA and against traditional averaging, on both real Mental Rotation data from 11 participants (Shepard & Metzler style stimuli) and simulated data, using time as a covariate. Pre-processing of real EEG data was conducted in Fieldtrip and simulated data was created in BESA simulator using a basic model of EEG data loosely based on neurologically plausible generators as identified by a meta-analysis of mental rotation localisation research. RESULTS Both covariate approaches were superior to traditional averaging in characterising mental rotation related components of the ERP across time. The added time-frequency resolution of WOPTA over the OPTA technique was able to capture more temporal and frequency specific data whilst largely maintaining cross-frequency coupling and coherency in the ERP data. CONCLUSIONS WOPTA has the potential to tap into ERP dynamics in a novel way as it not only improves SNR in comparison to averaging, but also reveals the deep covariance structure of the data. It is an improvement to OPTA, which itself has a clear advantage over traditional averaging. Keywords: WOPTA, wavelet analysis, ERPs, mental rotation, Learning Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Poster Topic: Other Citation: Provost A, Paton B, Karayanidis F, Brown S and Heathcote A (2013). Using Orthogonal Polynomial Trend Analysis and Wavelet decomposition (WOPTA) to investigate learning in a Mental Rotation task. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00139 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 Sep 2013; Published Online: 25 Nov 2013. * Correspondence: Mr. Alexander Provost, the University of Newcastle, Newcastle, Australia, alexander.provost@newcastle.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Alexander Provost Bryan Paton Frini Karayanidis Scott Brown Andrew Heathcote Google Alexander Provost Bryan Paton Frini Karayanidis Scott Brown Andrew Heathcote Google Scholar Alexander Provost Bryan Paton Frini Karayanidis Scott Brown Andrew Heathcote PubMed Alexander Provost Bryan Paton Frini Karayanidis Scott Brown Andrew Heathcote Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Dynamics of particle flux in a cylindrical magnetized plasma

Fluctuation-induced cross-field convective particle flux is studied in the cylindrical magnetized plasma assembly for non-linear turbulence analysis (PANTA). The plasma presents a wave turbulence characterized by a limited number of developing modes. To study the link between mode dynamics and particle transport, frequency-dependent time evolution of transport is obtained by applying complex continuous wavelet decomposition on spatio-temporal data from electrostatic probes. The method is shown to recover transport description consistent with other analysis methods for a weakly turbulent state. For a plasma state featuring a large non-sinusoidal structure, the time evolution of transport on the second harmonic of the main fluctuation shows a slow modulation of azimuthal mode number m = 0 with similarities to a zonal perturbation.

Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data

Recent studies have demonstrated that the decomposition of hyperspectral data using wavelet analysis is able to generate wavelet coefficients that can be used for estimating leaf chlorophyll (chl) concentrations. However, there is considerable scope for refining such techniques and this study addresses this issue by identifying the optimal spectral domain for use in constructing predictive models. Leaf reflectance spectra were simulated with the PROSPECT model (a model of leaf optical properties spectra) using randomly selected values for the input parameters. From reflectance and first derivative spectra different spectral wavelength domains were extracted, ranging from 400–450 to 400–2500 nm, using increments of 50 nm for the upper wavelength limit. Using the data for each wavelength domain, continuous wavelet decomposition was applied using 53 different wavelets, in turn. The resulting wavelet coefficients, from scales 1 to 128, were used as independent factors to construct predictive models for leaf chl concentration. Wavelet coefficients (at a specific scale generated by a given wavelet) in the chl absorption region remain constant when using spectral wavelength domains of 400–900 nm and broader, but narrower domains cause variability in the coefficients. Lower scale wavelet coefficients (scales 1–32) contain little information on chl concentration and their predictive performance does not vary with the spectral wavelength domain used. The higher scale wavelet coefficients (scales 64 and 128) can capture information on chl concentration, and predictive capability increases rapidly when the spectral wavelength domains vary from 400–700 to 400–900 nm but it can decrease or fluctuate for broader domains. In terms of accuracy and computational efficiency, models derived from the spectral wavelength domain 400–900 nm which use wavelet coefficients from scale 64 are optimal and a range of wavelet functions are suitable for performing the decomposition. The importance of optimizing the spectral wavelength domain highlighted by these findings has broader significance for the use of wavelet decomposition of hyperspectral data in quantifying other vegetation biochemicals and in other remote sensing applications.

Spike–wave discharges in WAG/Rij rats are preceded by delta and theta precursor activity in cortex and thalamus

Objective In order to unravel the mechanisms underlying the “sudden” onset of spontaneous absences in genetically prone subjects, we investigated the immediate precursors of spike–wave discharges (SWDs) produced in cortico-thalamo-cortical neuronal networks. Methods A time–frequency analysis of the cortical and thalamic ECoG of WAG/Rij rats was accomplished with a continuous wavelet decomposition of SWDs, 3 s prior to the onset of SWDs (pre-SWD), and in control periods devoid of SWDs. Results The pre-SWD ECoG consisted of delta and theta components in 80–90% of all SWDs simultaneously in cortex and thalamus, the co-occurrence of delta and theta was rare (7%) during control periods. The occurrence of delta and theta events in pre-SWDs in the cortex preceded that in the thalamus. The frequency of theta component in cortex correlated positively with that in thalamus, this correlation was less strong for delta. Conclusion Precursors of SWDs comprise of delta and theta, their co-occurrence is typical for non-epileptic periods. Thalamic and cortical theta are strongly related. Rhythmic precursors appear earlier in cortex than in thalamus, and this is in line with the cortical origin of SWD. Significance Simultaneous presence of delta and theta events in EEG is a condition for the occurrence of SWDs.