Maximal Overlap Discrete Wavelet Transform Research Articles

Probabilistic deep learning approach for fatigue crack width estimation and prognosis in lap joint using acoustic waves

Abstract Accurate fatigue crack width estimation is crucial for aircraft safety, however, limited research exists on (i) the direct relationship between fatigue crack width and Lamb wave signatures and (ii) probabilistic artificial intelligence (AI) approach for automated analysis using acoustic emission waveforms. This paper presents a probabilistic deep learning approach for fatigue crack width estimation, employing an automated wavelet feature extractor and probabilistic Bayesian neural network. A dataset constituting the fatigue experiment on aluminum lap-joint specimens is considered, in which Lamb wave signals were recorded at several time instants for each specimen. Signals acquired from the piezo actuator-receiver sensor pairs are related to the optically measured surface crack length. The sensitive features are automatically extracted from the signals using decomposition techniques called maximal overlap discrete wavelet transform (MODWT). The extracted features are then mapped through the deep learning model, which incorporates Bayesian inference to account for both aleatoric as well as epistemic uncertainty, that provides outcomes in the form of providing probabilistic estimates of crack width with uncertainty quantification. Thus, employing an automated wavelet feature extractor (MODWT) on a dataset of fatigue experiments, the framework learns the relationship between Lamb wave signals and crack width. Validation on unseen in-situ data demonstrates the efficacy of the approach for practical implementation, paving the way for more reliable fatigue life prognosis.

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings

Objective. Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters. Approach. Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients. Main result. Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data. Significance. Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.

LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites

Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency.

Quantification of impedance and mechanical properties of Zeonor using scanning acoustic microscopy

This article explores the potential of Zeonor, a transparent cyclic olefin polymer, for applications in transparent acoustic/photo-acoustic transducers. Despite its widespread use in optics, microfluidics, medical devices, and electronics, Zeonor's acoustic properties remain understudied. To extend this, the current work employs scanning acoustic microscopy (SAM) to obtain its mechanical properties. The signal processing method known as maximal overlap discrete wavelet transform (MODWT) is applied to efficiently break down the acoustic responses of Zeonor. The filtered signal is now time-segmented to obtain the desired signal, containing the characterizing signal feature that predicts the acoustic impedance. Further, the longitudinal and transverse acoustic velocities of the Zeonor are determined using successive peak identification and time-of-flight observations. Moreover, an impedance map is developed using Kriging with a Gaussian variogram, that provides the spatial distribution of impedance. Utilizing these results, the article determines mechanical properties such as shear modulus, Young's modulus, and Poisson's ratio, finding good agreement with literature values. The lower acoustic impedance and optical transparency of Zeonor position it as an ideal material for high-frequency photo-acoustic transducers.

The Co-volatility of Uncertainty Shock on Chinese Exchange Rate: Based on Time-frequency Domain and Time-varying Method

In recent years, with the global epidemic, the Russia-Ukraine conflict, the great power game, and so on events, the international political and economic environment has become more and more unstable, the USDCNY exchange rate fluctuates significantly, and the external uncertainty risk shocks negatively hit the foreign exchange market. Based on this, this article expands global uncertainty risk shocks into four dimensions: global geopolitical risk, U.S. economic policy uncertainty, Chinese economic policy uncertainty, and VIX index, in addition to adding international WTI crude oil and gold price as additional variables, which are significantly correlated with both, to study the linkage among uncertainty shocks, commodity volatility and USDCNY exchange rate from the time-frequency domain. Furthermore, we analyze the time-varying spillover effects among variables at different frequency scales by decomposing the data into multiple scales using the Maximum Overlap Discrete Wavelet Transform (MODWT). Findings reveal that uncertainty factors and commodity prices exert distinct influence mechanisms on the USDCNY exchange rate under different cycles of frequency fluctuations. At various time-frequency domain volatility scales, VIX and WTI generate significant risk spillovers over the USDCNY exchange rate. Moreover, when global major events occur, time-varying spillover effects of variables are also heterogeneous. The research findings presented in this paper can provide valuable insights for policymakers and investors regarding the impact of international uncertainty shocks on the Chinese exchange rate across various short, medium, and long frequencies. This understanding can aid in managing uncertainty risks in decision-making processes, ultimately facilitating the efficient functioning of the foreign exchange market.

Through the wall human heart beat detection using single channel CW radar.

Single-channel continuous wave (CW) radar is widely used and has gained popularity due to its simple architecture despite its inability to measure the range and angular location of the target. Its popularity arises in the industry due to the simplicity of the required components, the low demands on the sampling rate, and their low costs. Through-the-wall life signs detection using microwave Doppler Radar is an active area of research and investigation. Most of the work in the literature focused on utilizing multi-channel frequency modulated continuous wave (FMCW), CW, and ultra-wideband (UWB) radar for their capability of range and direction of arrival (DOA) estimation. In this paper, through-the-wall single-subject and two-subject concurrent heart rate detection using single-channel 24-GHz CW radar leveraged with maximal overlap discrete wavelet transform (MODWT) is proposed. Experimental results demonstrated that the repetitive measurement of seven different subjects at a distance of 20cm up to 100cm through two different barriers (wood and brick wall) showed an average accuracy of heart rate extraction of 95.27% for varied distances (20-100cm) in comparison with the Biopac ECG acquisition signal. Additionally, the MODWT method can also isolate the independent heartbeat waveforms from the two subjects' concurrent measurements through the wall. This involved four trials with eight different subjects, achieving an accuracy of 97.04% for a fixed distance of 40cm from the Radar without estimating the angular location of the subjects. Notably, it also superseded the performance of the direct FFT method for the single subject after 40cm distance measurements. The proposed simpler architecture of single-channel CW radar leveraged with MODWT has several potential applications, including post-disaster search and rescue scenarios for finding the trapped, injured people under the debris, emergency evacuation, security, surveillance, and patient vital signs monitoring.

Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique

The automated analysis of optical fiber vibration sensing data has been highly demanded in engineering applications. Therefore, intrusion analysis, which aims at detecting, recognizing, and classifying intrusions, holds great importance for optical fiber vibration sensing. In this work, an intelligent intrusion detection scheme employing an improved high-efficiency feature extraction technique and utilizing a dual Mach–Zehnder interferometer (DMZI)-based optical fiber perimeter security system is proposed. So, the DMZI-based perimeter security system in practical settings can be successfully established. Specifically, time-frequency feature vectors with nine features are firstly constructed using a maximal overlap discrete wavelet transformation approach and a zero crossing rate method. Then, the feature vectors are classified into corresponding categories using a radial basis function neural network. The effectiveness of the proposed scheme has been validated using six types of human intrusions, such as knocking, climbing, waggling, cutting, crashing and kicking the fence. The results show that the given intrusions can be accurately and rapidly recognized by the proposed scheme. The average recognition rate of 95.0% is achieved, and the average processing time for each sample data is only 0.033 s, which is significantly lower than the sampling interval (0.3 s) in our experiment. It is believed that the proposed scheme holds promising potential in the field of optical fiber perimeter security systems.

A novel granular decomposition based predictive modeling framework for cryptocurrencies' prices forecasting

PurposeOwing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.Design/methodology/approachInitially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.FindingsRigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.Originality/valueThe robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

Combined Maximal Overlap DWT and Adaptive Filtering for Denoising Seismic Signals

In this paper, denoising of seismic signals based on Maximal Overlap Discrete Wavelet Transform and Adaptive Filtering (MODWT-AF) is discussed. In this method, first the seismic signals are converted into a set of coefficients called approximation and detailed coefficients by applying MODWT-based Multi Resolution Analysis (MODWT-MRA) technique. Then, adaptive filtering is applied to these coefficients, which removes noise present in the frequency sub bands of the signals. Finally, the denoised signal is reconstructed by performing the inverse MODWT to the modified set of coefficients. As a statistical analysis, the performance measures such as input signal-to-noise , output signal-to-noise ratio , SNR improvement , Normalized Correction Coefficient (NCC), Mean Square Error (MSE), and RMS error are evaluated for different real earthquake records and synthetic seismic signals. The performance results of the proposed methodology are compared with other conventional thresholding methods such as MODWT-based thresholding (MODWT-TH) and Discrete Wavelet Transform based thresholding techniques namely Heursure (DWT-HE), Sqtwolog (DWT-SG), Minimaxi (DWT-MI), and Rigrsure (DWT-RE). The experimental results effectively demonstrated that the proposed methodology produced better outcomes compared to the conventional methods.

River water temperature prediction using hybrid machine learning coupled signal decomposition: EWT versus MODWT

Accurate prediction of water temperature (Tw) will greatly help in improving our understanding of the overall thermal regime fluctuation, and it can help in making sound decisions. While great efforts have been devoted to the development of Tw models, further improvement in the prediction accuracy is challenging. Here, we propose a new hybrid machine learning (ML) models for predicting Tw from air temperature (Ta). First, two signal decomposition algorithms, i.e., the empirical wavelet transform (EWT) and maximum overlap discrete wavelet transform (MODWT) are used for decomposing the Ta into several subsequences. Second, the obtained subsequences are used as input variables for four ML models, i.e., the feedforward neural network (FFNN), the optimally pruned extreme learning machine (OPELM), the adaptive boosting (AdaBoost), and the bootstrap aggregating (Bagging). The development of the hybrid models is based on measured data form five measurement stations distributed over Poland. The experimental results show that, the new hybrid ML models achieved high predictive accuracies with Pearson correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE) of approximately ≈0.99, ≈0.98, ≈0.718 °C, and ≈0.599 °C, respectively. Finally, the experimental results have demonstrated that, the MAE and RMSE of the single models were reduced by 66.12% and 65.30%, while the R and NSE values were improved by 4.85% and 10.02%, respectively, showing a very promising prediction performance. Our new modelling approach proposed in the present study clearly highlight the high contribution of the EWT and MODWT in improving the Tw estimation that is most strongly influenced by Ta. The significant difference between single and hybrid models can be explained by the ability of the EWT and MODWT to capture the high nonlinearity between Ta and Tw. Our new approach is of interest for water resources management and for assessing the variability of Tw over time and space. Finally yet importantly, this is the first study in the literature for which EWT and MODWT were used for modelling Tw, through which we have demonstrated that, an important amount of information is available and cannot be captured using single ML models, and signal decomposition have helped to overcome this challenge.

Spillover effects from China and the United States to Key Regional Emerging Markets: A dynamic analysis

While a limited number of studies have employed wavelet techniques to distinguish between market interdependence and contagion within the context of market co-movement, many of them have primarily relied on conclusions derived from multivariate wavelet analysis. In contrast, this paper introduces a complementary approach based on the univariate wavelet technique, specifically the Maximal Overlap Discrete Wavelet Transform (MODWT). This approach entails assessing the correlation between key market series that have been decomposed into both the time and frequency domains. The proposed technique is applied to evaluate the co-movement and dynamic spillover effects from two prominent advanced markets, namely China and the United States, to six major emerging economies across different regions. The findings obtained through various wavelet techniques consistently support the notion that interdependence is the predominant characteristic in terms of shock spillovers and stock market co-movement between the selected advanced and key emerging markets. Furthermore, the results indicate that both the US and China cycles are synchronized with those of the key emerging economies. These insights are of significant importance for investors and asset managers, offering valuable guidance for asset diversification and portfolio allocation strategies during both tranquil and turbulent periods.

Non-Invasive Blood Pressure Sensing via Machine Learning.

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.

Siamese Neural Network for User Authentication in Field-Programmable Gate Arrays (FPGAs) for Wearable Applications

User authentication has traditionally been performed using methods such as passwords or fingerprints. However, passwords have security vulnerabilities, and fingerprints may hinder user convenience. To address these issues, a novel user authentication method based on biosignals, specifically electromyogram (EMG) signals, is proposed. Using biosignals like EMG offers several advantages, including the ability to acquire data without user awareness, independence from the user’s environment, rapid acquisition, and enhanced security. However, one challenge with using EMG signals for authentication has been their relatively low accuracy. In this paper, a neural network is implemented using a small number of parameters (fewer than 7000) to produce a wearable device using biosignals, and user authentication accuracy is secured using the maximal overlap discrete wavelet transform (MODWT) method and the Siamese network. The MODWT method is highly effective for the time and frequency analysis of time series data, and the Siamese network is a representative method for few-shot learning. The proposed neural network is verified using Chosun University’s user authentication dataset, encompassing data from 100 individuals. Finally, this proposed network is implemented on an edge device such as field-programmable gate arrays (FPGAs) so that it can be applied to a wearable user authentication system. By implementing the Siamese network in FPGA-based edge devices, it was possible to secure user authentication performance at 94% accuracy and an authentication speed within 1.5 ms. In the case of accuracy, it is expected to be further improved by using the multimodal technique of biosignals. Also, the proposed system can be easily fabricated for digital integrated chips (ICs).

An explainable AI-enabled granular ensemble machine learning framework to demystify fertilizer price movements

This paper proposes a novel explainable artificial intelligence (AI) driven ensemble machine learning (ML) framework for predicting fertilizer price movements and assessing the contributions of the technical and macroeconomic indicators. We integrate the Boruta algorithm, Maximal Overlap Discrete Wavelet Transformation (MODWT), Random Forest (RF), and explainable AI. The predictive analytics exercise utilises the residual of the previous stage as an additional indicator for arriving at the subsequent stage forecasts. We observe a significant influence of the residual in providing forecasts for time series with higher frequencies. The explainable AI is used at the global and local levels to explain the impacts of the indicators on fertilizer price movements. We have used monthly urea and diammonium phosphate (DAP) prices for nearly the last 30 years for predictive analytics. The explainable AI identifies the more significant impacts of the technical indicators compared to macroeconomic counterparts in forecasting urea and DAP prices at the global level. Also, the price movements of urea and DAP are similar at the global level. On the contrary, macroeconomic indicators influence more at the local level. The CBOE volatility index for urea, geopolitical risk, and commodity industrial input for DAP significantly influence the price movements at the local level.

Time Scales Based Analysis of the Effects of COVID-19 Related Economic Support on the Stock Markets in Emerging Markets

Abstract The main purpose of this study is to investigate the causal response of the stock market returns to COVID-19 related economic support in 19 emerging countries by using the Maximal Overlap Discrete Wavelet Transform (MODWT) and Fourier Toda-Yamamoto Causality Test (FTYCT). With the help of MODWT, we identify the instant, short-term, mid-term and long-term reactions of stock market returns and COVID-19 related economic support to each other. Implementing FTYCT, we determine the existence of the causal relationships running from COVID-19 related economic support to stock returns. We obtain two major results. First, the COVID-19 related economic support have significant effects on stock market returns in the short-, medium-, and long-term, except in China. Second, the results of the causality tests vary across countries based on the different time scales. Some emerging markets show an immediate reaction to the Economic Support, while most stock market reactions occur over the medium- and long-term. Since economic support will created unintended effects on stock market returns, the way that these support policies are implemented should be reconsidered. Also, their effectiveness should be evaluated carefully.

A Wavelet-Decomposed WD-ARMA-GARCH-EVT Model Approach to Comparing the Riskiness of the BitCoin and South African Rand Exchange Rates

In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss.

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 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.