Maximum Overlap Research Articles

The Linkage between Carbon Market and Green Bond Market: Evidence from Quantile Regression Based on Wavelet Analysis

The carbon market and the green bond market are important institutions for reducing greenhouse gas emissions and achieving economic low-carbon transformation. Accurately understanding the characteristics and correlations of the two markets is of great significance for promoting the achievement of the “dual carbon” goal. From the perspective of different time scales and market conditions, this study selected the maximal overlap discrete wavelet transform (MODWT) to decompose the price time series data of China’s carbon market and green bond market. The quantile Granger causality test was used to calculate the causal relationship between the two markets at different quantiles, and the association between the two markets was estimated based on quantile-to-quantile regression (QQR). The results show that, regardless of the time scale and market conditions, the Chinese carbon market is always the Granger cause of the green bond market. When the green bond market is in a slump state (i.e., in a “bear” market), it will have a certain negative impact on the carbon market in the short term, but in the medium and long term, the impact of the green bond market on the carbon market is positive. In addition, as the time scale increases, the synergistic effect between the green bond market and the carbon market becomes more and more significant. At medium- to long-term time scales, extreme market conditions can easily cause extreme shocks from the green bond market to the carbon market.

The isotropy of cryptocurrency volatility

AbstractWe examine the fractal volatility and long‐range dependence of Bitcoin, Ethereum, Tether and USD Coin by employing the continuous wavelet transform, maximal overlap discrete wavelet transform and rescaled range. Our dataset consists of daily prices spanning from January 2017 through to October 2022, encapsulating pre‐ and post‐epidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the time‐frequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the post‐epidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and long‐term fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of self‐similarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policy‐related events differ between them, as both return series move incongruously. Conditional dynamics signify that all cryptocurrencies, except Tether, were affected by the pandemic transition of COVID‐19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zero‐mean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate one‐dimensional lines, whereas stablecoins mimic two‐dimensional planes.

Quantum Entanglement and State-Transference in Fenna–Matthews–Olson Complexes: A Post-Experimental Simulation Analysis in the Computational Biology Domain

Fenna-Mathews-Olson complexes participate in the photosynthetic process of Sulfur Green Bacteria. These biological subsystems exhibit quantum features which possibly are responsible for their high efficiency; the latter may comprise multipartite entanglement and the apparent tunnelling of the initial quantum state. At first, to study these aspects, a multidisciplinary approach including experimental biology, spectroscopy, physics, and math modelling is required. Then, a global computer modelling analysis is achieved in the computational biology domain. The current work implements the Hierarchical Equations of Motion to numerically solve the open quantum system problem regarding this complex. The time-evolved states obtained with this method are then analysed under several measures of entanglement, some of them already proposed in the literature. However, for the first time, the maximum overlap with respect to the closest separable state is employed. This authentic multipartite entanglement measure provides information on the correlations, not only based on the system bipartitions as in the usual analysis. Our study has led us to note a different view of FMO multipartite entanglement as tiny contributions to the global entanglement suggested by other more basic measurements. Additionally, in another related trend, the initial state, considered as a Förster Resonance Energy Transfer, is tracked using a novel approach, considering how it could be followed under the fidelity measure on all possible permutations of the FMO subsystems through its dynamical evolution by observing the tunnelling in the most probable locations. Both analyses demanded significant computational work, making for a clear example of the complexity required in computational biology.

The Wavelet Multi-Scale Analysis of Exchange Rate Exposure: An Application to Malaysian Consumer Products and Services Sector

Objective – Past efforts in assessing foreign exchange rate exposure assume homogeneity in the level of exposure across time horizons which seems to be impractical due to the dynamic nature of comovement between firm value and exchange rate. Methodology – Given this, the study aimed to investigate the multi-scale relationships between changes in exchange rates and firm values of 56 multinational corporations in the consumer products and services sector from January 2000 to December 2020. Findings – The novelty of the study lies upon the application of Maximal Overlap Discrete Wavelet Transformation (MODWT) method which allows decomposition of a single time series domain into different time domains. The fragmentation into multiple time domains allows the measurement of scale-dependent foreign exchange exposure. As a result, the study discovered a non-monotonic trend for wavelet scale j of currency exposure across time scales. In particular, there was a gradual increase in the magnitude of beta exchange exposure and the proportion of exposed firms from low to high time scales. This finding suggested that firm value is more sensitive to changes in the exchange rate within a widened time domain. Novelty – The study demonstrated on how the wavelet technique can be used to measure foreign exchange risk and aided firm managers and market participants in managing foreign exchange risk for a specific time interval. Type of Paper: Empirical JEL Classification: E42, F31, F39 Keywords: Currency exposure; multi-horizon exchange rate; Malaysian Sectors; Wavelet Analysis; MODWT. Reference to this paper should be made as follows: Wahab, H.A; Nasir, N.A.M. (2023). The Wavelet Multi-scale Analysis of Exchange Rate Exposure: An Application to Malaysian Consumer Products and Services Sector, J. Fin. Bank. Review, 8(1), 58 – 72. https://doi.org/10.35609/jfbr.2023.8.1(3)

A granular machine learning framework for forecasting high-frequency financial market variables during the recent black swan event

This paper analyses highly voluminous 1-minute intraday movements of the closing prices of Bitcoin, crude oil, the Dow Jones Industrial Average (DJIA) and the euro–U.S. dollar exchange rate in various non-overlapping regimes spanning across the COVID-19 pandemic, a recent black swan event. The empirical characteristics of the intraday dynamics of chosen assets are gauged using nonlinear modelling tools and tests. The proposed methodological framework utilises maximal overlap discrete wavelet transformation, Bayesian structural time series forecasting and random walk with drift to evaluate the quantum of predictability of select variables in different phases across the pandemic horizon. The framework survives a series of performance and statistical checks to justify the efficacy of drawing highly accurate predictions from big data setups. The findings suggest that, despite chaotic traces, all variables can be precisely forecast across different sub-regimes, eliminating the adverse effects of the first and second waves of the COVID-19 pandemic. High-frequency movements of Bitcoin and Euro–U.S. dollar exchange rates are relatively better predictable, signifying resilience during the pandemic. Finally, the outcome of this research will help mitigate financial risk during volatile periods.

Construction of Asymmetrical Dual Jahn-Teller Sites for Photocatalytic CO2 Reduction.

Photocatalytic CO2 reduction (PCR) expresses great attraction to convert useless greenhouse gas into valuable chemical feedstock. However, the weak interactions between catalytic sites and PCR intermediates constrains the PCR activity and selectivity. Herein, we proposed a new strategy to match the intermediates due to the maximum orbital overlap of catalytic sites and C1 intermediates by establishing dual Jahn-Teller (J-T) sites, in which, the strongly asymmetric J-T sites can break the nonpolar CO2 molecules and self-adapt the different structure of C1 intermediates. Taking cobalt carbonate hydroxide as an example, the weakly symmetric dual cobalt (Co2 ) dual J-T sites, weakly asymmetric Fe&Co sites and strongly asymmetric Cu&Co sites were assembled. After illumination, the interaction between dual J-T sites and the CO2 molecules enhances J-T distortion, which further modulates the PCR activity and selectivity. As a result, the Cu&Co sites exhibited CO yield of 8137.9 μmol g-1 , about 2.3-fold and 4.2-fold higher than that of the Fe&Co and Co2 sites within 5-hour photoreaction, respectively. In addition, the selectivity achieved as high as 92.62 % than Fe&Co (88.67 %) and Co2 sites (55.33 %). This work provides a novel design concept for the construction of dual J-T sites to regulate the catalytic activity and selectivity.

Construction of Asymmetrical Dual Jahn–Teller Sites for Photocatalytic CO2 Reduction

AbstractPhotocatalytic CO2 reduction (PCR) expresses great attraction to convert useless greenhouse gas into valuable chemical feedstock. However, the weak interactions between catalytic sites and PCR intermediates constrains the PCR activity and selectivity. Herein, we proposed a new strategy to match the intermediates due to the maximum orbital overlap of catalytic sites and C1 intermediates by establishing dual Jahn–Teller (J–T) sites, in which, the strongly asymmetric J–T sites can break the nonpolar CO2 molecules and self‐adapt the different structure of C1 intermediates. Taking cobalt carbonate hydroxide as an example, the weakly symmetric dual cobalt (Co2) dual J–T sites, weakly asymmetric Fe&Co sites and strongly asymmetric Cu&Co sites were assembled. After illumination, the interaction between dual J–T sites and the CO2 molecules enhances J–T distortion, which further modulates the PCR activity and selectivity. As a result, the Cu&Co sites exhibited CO yield of 8137.9 μmol g−1, about 2.3‐fold and 4.2‐fold higher than that of the Fe&Co and Co2 sites within 5‐hour photoreaction, respectively. In addition, the selectivity achieved as high as 92.62 % than Fe&Co (88.67 %) and Co2 sites (55.33 %). This work provides a novel design concept for the construction of dual J–T sites to regulate the catalytic activity and selectivity.

Linear scaling of charge transfer versus work function of Ammonia chemisorption on X-metal (X = Ag, Au, Pd, and Pt) surface

Although the d-band center theory can efficiently describe the interaction between gas molecules and transition metal surfaces, the specific reaction process and detailed adsorption criteria remain unclear. Therefore, we systemically investigated the adsorption process and mechanism of Ammonia (NH3) molecules on transition metal (i.e., Ag, Au, Pd and Pt) surfaces using first-principles calculations. Results indicate that the NH3 molecule undergoes chemisorption on the Pt (111) surface with considerable charge transfer and adsorption energy, indicating that Pt metal is a promising NH3-sensitive material. Furthermore, the chemisorption primarily entails the highest-occupied molecular orbital (HOMO) of NH3, and the metal atoms conform perfectly to the principles of energy-level matching, wave function matching, and maximal orbital wave function overlap. Notably, a linear relationship exists between the charge transfer of NH3 and the work function of X-metal substrates. Our findings provide a feasible way to efficiently design NH3-sensitive materials for experiments.

A novel rolling bearing fault diagnosis method based on Adaptive Denoising Convolutional Neural Network under noise background

In recent years, significant progress has been made in intelligent fault diagnosis algorithms for rolling bearings. However, their real industrial application performance is hindered by challenges related to noise and variable load conditions. To solve this problem, we proposed an adaptive denoising convolutional neural network (ADCNN) which integrates adaptive denoising units to remove noise while preserving sensitive fault features, eliminating the need for manual denoising function settings. In addition, we use Maximum Overlap Discrete Wavelet Packet Transform to separate out the interfering components of noisy signal. To further improve ADCNN's noise immunity, we adopt a strategy of gradually decreasing the number of channels and using large convolutional kernels. ADCNN was evaluated alongside the latest methods on two different datasets, and the results demonstrate that ADCNN outperforms other methods both accuracy and robustness. Therefore, our approach presents a promising solution for diagnosing mechanical systems in noisy environments.

Intelligent fault classification of air compressors using Harris hawks optimization and machine learning algorithms

Due to their complexity and often harsh working environment, air compressors are inevitably exposed to a variety of faults and defects during their operation. Thus, condition monitoring is critically required for early fault recognition and detection to avoid any type industrial failures. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is developed using real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states such as leakage inlet valve (LIV), leakage outlet valve (LOV), non-return valve (NRV), piston ring, flywheel, rider-belt and bearing defects. The proposed algorithm mainly consists of three steps: feature extraction, selection, and classification. For feature extraction, experimental acoustic signals are decomposed using maximal overlap discrete wavelet packet transform (MODWPT) by six levels into 64 wavelet coefficients (nodes). Thereafter, time domain features are calculated for each node to build each air compressor’s health state feature matrix. Each feature matrix dimension is reduced by selecting the most useful features using Harris hawks optimization (HHO) in the feature selection step. Finally, for feature classification, selected features are used as inputs for random forest (RF), ensemble tree (ET) and K-nearest neighbors (KNN) to detect, identify, and classify the compressor health states with high classification accuracy. Comparative studies with several feature extraction and selection methods prove the proposed approach’s efficiency in detecting, identifying, and classifying all air compressor faults.

Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks.

Activated channels of functional near-infrared spectroscopy are typically identified using the desired hemodynamic response function (dHRF) generated by a trial period. However, this approach is not possible for an unknown trial period. In this paper, an innovative method not using the dHRF is proposed, which extracts fluctuating signals during the resting state using maximal overlap discrete wavelet transform, identifies low-frequency wavelets corresponding to physiological noise, trains them using long-short term memory networks, and predicts/subtracts them during the task session. The motivation for prediction is to maintain the phase information of physiological noise at the start time of a task, which is possible because the signal is extended from the resting state to the task session. This technique decomposes the resting state data into nine wavelets and uses the fifth to ninth wavelets for learning and prediction. In the eighth wavelet, the prediction error difference between the with and without dHRF from the 15-s prediction window appeared to be the largest. Considering the difficulty in removing physiological noise when the activation period is near the physiological noise, the proposed method can be an alternative solution when the conventional method is not applicable. In passive brain-computer interfaces, estimating the brain signal starting time is necessary.

Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The lack of specific drugs and ready-to-use vaccines to prevent most of these epidemics worsens the situation. These force public health officials and policymakers to rely on early warning systems generated by accurate and reliable epidemic forecasters. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyze various epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it Ensemble Wavelet Neural Network (EWNet) model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with twenty-two statistical, machine learning, and deep learning models for fifteen real-world epidemic datasets with three test horizons using four key performance indicators. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods.

Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention

Accurate wind speed forecasting (WSF) is important for effectively harnessing wind energy with clean and sustainable energy benefits. Therefore, this study develops different models established through the use of correlation analysis (CA) and decomposition techniques, Harris hawks optimization algorithm (HHO), and S2S (sequence2sequence) based spatial and temporal attention (STAt-S2S) for effective WSF. First, the CA selects variables of significant correlation with the wind speed data. In the next stage, improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) and discrete wavelet transform with maximum overlap (MODWT) techniques are employed to decompose the components having significant correlation. Afterwards, HHO selects suitable features from the decomposed data. Finally, STA-S2S extracts spatial, temporal features and performs forecasting. The CA-ICEEMDAN–HHO–STAt-S2S and CA-ICEEMDAN-STAt-S2S models reveal better forecasting outcomes over the other standalone and hybrid foresting models. The RMSE, MAE, and sMAPE values presented by CA-ICEEMDAN-STAt-S2S are 0.639 m/s, 0.474 m/s and 15.710 m/s with NSE of 0.922. The lowest error values with the highest efficiency values of ICEEMDAN, HHO, and STAt-S2S-based hybrid models corroborate the feasibility of these models for WSF with equal applicability for similar time series applications.

Epileptic seizure prediction based on multiresolution convolutional neural networks

Epilepsy withholds patients’ control of their body or consciousness and puts them at risk in the course of their daily life. This article pursues the development of a smart neurocomputational technology to alert epileptic patients wearing EEG sensors of an impending seizure. An innovative approach for epileptic seizure prediction has been proposed to improve prediction accuracy and reduce the false alarm rate in comparison with state-of-the-art benchmarks. Maximal overlap discrete wavelet transform was used to decompose EEG signals into different frequency resolutions, and a multiresolution convolutional neural network is designed to extract discriminative features from each frequency band. The algorithm automatically generates patient-specific features to best classify preictal and interictal segments of the subject. The method can be applied to any patient case from any dataset without the need for a handcrafted feature extraction procedure. The proposed approach was tested with two popular epilepsy patient datasets. It achieved a sensitivity of 82% and a false prediction rate of 0.058 with the Children’s Hospital Boston-MIT scalp EEG dataset and a sensitivity of 85% and a false prediction rate of 0.19 with the American Epilepsy Society Seizure Prediction Challenge dataset. This technology provides a personalized solution for the patient that has improved sensitivity and specificity, yet because of the algorithm’s intrinsic ability for generalization, it emancipates from the reliance on epileptologists’ expertise to tune a wearable technological aid, which will ultimately help to deploy it broadly, including in medically underserved locations across the globe.

Integration of macroeconomic fundamentals in the West African Monetary Zone: A multi scale perspective

A recently developed wavelet multiple correlations and cross correlations based on the maximal overlap discrete wavelet transform by Fernández-Macho is employed to analyse the interdependence of macroeconomic fundamentals among countries within the West African Monetary Zone for data from January 2000 to December 2018. We establish that the nature of interdependence among exchange rates, inflation, and interest rates vary across the countries. We document that the overall correlations of the three macroeconomic variables are weak in the short term, medium term, and long term. There is evidence of weaker integration at all frequencies for exchange rates, inflation, and interest rates.However, Ghana has the maximum wavelet multiple correlations at these times. The overall correlation of macroeconomic variables shows inconsistencies and may be accounted for by varying factors which often exert influence on market linkages over time. It is clear from the empirical evidence that the ex-ante conditionality for the introduction of the single currency in West African Monetary Zone will be difficult to achieve.

On the similarities between precious metals, precious metal stocks and equities – International evidence for gold and silver

The study attempts to assess if the precious metal stocks behave more like their metal or stock counterparts. We use both Maximal Overlap Discrete Wavelet Transform and Continuous Wavelet Transform to test the time and frequency-varying association of precious metal stocks with the precious metals and broader equity indices. The empirical shreds of evidence are suggestive that precious metal stocks behave more like precious metals than common stocks. The results bear important policy implications for the multi-horizon investors wishing to expose themselves to precious metal stocks, precious metals and common stocks.

Application of wavelet to strength log from scratch test

This paper proposes a methodology to construct logs of rock strength from the cutting force signal recorded in scratch tests conducted in the ductile regime. The approach, which is based on the application of discrete wavelet transforms, recognizes the existence of two length scales ℓc and ℓr. The strength length scale ℓc represents the length over which it is meaningful to measure strength, while the repeatability length scale ℓr is related to the resolution at which the force signal must be observed to become insensitive to the stochastic micro-failure events triggered by the motion of the cutter. It is postulated that the original cutting force signal, assumed to be sampled at a high enough frequency, can be decomposed into a deterministic signal intrinsic to the rock and a stochastic one resulting from discrete rock failure events. The technique of multiresolution analysis based on the maximal overlap discrete wavelet transform is applied as a low-pass filter to the original cutting force signals so as to wipe out the high-frequency components associated with the stochastic rock failure events. A criterion to determine the optimum cutoff frequency of the low-pass filter and the corresponding repeatability length scale is discussed in terms of the correlation coefficients between different filtered signals. It is shown that the low-pass filtered signals obtained at the optimum cutoff frequency have two salient features: (i) repeatability over different tests conducted at the same depth of cut on the same sample, and (ii) variability along the cutting distance. The excellent repeatability reveals that the deterministic background trend of the original force signals is relevant to the rock strength property, and the variability of the background trend captures the spatial variation of the rock strength.

Comparison of Methods ARIMA and MAR Models with MODWT Decomposition on Non-Stationary Data

The forecasting methods used in this study are Autoregressive Integrated Moving Average (ARIMA) and Multiscale Autoregressive (MAR). The ARIMA model does not include predictor variables in the model. The MAR model is a model that performs the transformation process using wavelets. The MAR model adopts an autoregressive time series (AR) model with wavelet coefficients and scale coefficients as predictors. The wavelet coefficient and scale are obtained by decomposition using Maximal Overlap Discrete Wavelet Transformation (MODWT). MODWT functions to describe data based on the level of each wavelet filter. This study aims to determine the best forecasting model using ARIMA and MAR models. The time series data used in this study is data on the rupiah exchange rate against the US dollar. Data on the rupiah exchange rate against the US Dollar for 2019-2020 is non-stationary data, so the ARIMA and MAR models can be used in this study.

Two terminals protection method for HVDC transmission lines based on maximal overlap discrete wavelet transform and cross-correlation coefficient of traveling wave

This study presents a novel method of HVDC protection based on the maximal overlap discrete wavelet transform (MODWT) and the kurtosis of cross correlation coefficient. Compared with the discrete wavelet transform (DWT), this method does not have data down sampling, and can realize fault identification and fault location at the same time. According to the high frequency content is different under different fault types and different fault area, firstly use of MODWT four layers of fault traveling wave decomposition, the double side of the decomposed high frequency component cross-correlation analysis; subsequently, kurtosis ratio relations to each other to judge fault zone and the fault types, the influence of noise on the protection method is also considered; finally, the maximum coordinate of cross-correlation function is used to calculate the fault location. At the same time, this paper analyzes the reduction of ranging accuracy caused by the decrease of sampling frequency, and further use of the interpolation algorithm for accurate positioning of internal fault. The simulation model of ±800 kV bipolar DC transmission is built in PSCAD/EMTDC, and the simulation analysis is carried out under different fault distance, transition resistance and fault type, the simulation results show that the protection method is not affected by the transition resistance, fault type and noise, and the fault location is more accurate.

Electricity price forecasting using hybrid deep learned networks

AbstractThis paper presents a novel hybrid model integrating maximal overlap discrete wavelet transform (MODWT) denoising and empirical mode decomposition (EMD) with sequence‐to‐sequence (seq2seq) long short‐term memory (LSTM) neural networks for day‐ahead electricity price forecasting. The nonstationary and nonlinear time series electricity price data are first denoised using MODWT. The resulting signal is decomposed into several intrinsic mode functions (IMF) with different resolutions by EMD. The extracted IMF is then introduced into seq2seq LSTM to obtain an aggregated predicted value for electricity price. The proposed method is examined using the Nord pool Elspot energy market data. Empirical results show that the proposed model outperformed the other forecasting models like LSTM and stacked LSTM. The performance measures indicate that data denoising can significantly improve the prediction stability and the generalization ability of the LSTM model.