Wavelet Coherence Approach Research Articles

Re-Visiting the Co-movement between FinTech, Economic Policy Uncertainty, Governance Quality and Bank Variability in Pakistan: A Wavelet Coherence Approach

The recent literature about FinTech services in Pakistani commercial banks has grabbed much attention as these services have increased rapidly in the last few decades. This paper examines the co-movement between FinTech, economic policy uncertainty, bank- variability, and governance quality. The data was obtained from 2014-2020 on a monthly frequency. To address the relationship and co-movement, we have applied more advanced and sophisticated econometric models, autoregressive distributed lag model (ARDL), and continuous wavelet (CTW) to investigate the relationship between the variables. These approaches provide flexibility of assumptions for time-series data and provide robust results. The results indicate a short and long-run symmetric relationship with a significant co-movement between the variables.

Asymmetric dynamic correlations and portfolio management between Bitcoin and stablecoins

ABSTRACT This study unveils the unique properties of crypto assets and investigates the dynamic connectedness between six prominent stablecoins and Bitcoin in comparison with Bitcoin/stablecoin pairs involving portfolio management. Empirically, using the DCC-GJR-GARCH and partial wavelet coherence approaches, we show that Bitcoin provides greater diversification potential benefits compared to stablecoins during the COVID-19 crisis. The evidence suggests that Bitcoin, as the dominant cryptoasset, can serve as a suitable asset for portfolio diversification against stablecoins. Besides, we evaluate potential hedging benefits of cryptocurrencies for market participants and find that stablecoins are poor hedging products in most of the cases considered. The optimal portfolio for the hedging strategy involving a mix of Bitcoin and stablecoins reveals that the weights assigned to stablecoins are lower than those for Bitcoin. In particular, our results provide timely implications for market participants whose crypto portfolios include stablecoins, especially after Terra’s collapse.

Assessment of the causal links between energy, technologies, and economic growth in China: An application of wavelet coherence and hybrid quantile causality approaches

It is undeniable that energy and technology play a vital role in driving the economic growth of a country. This paper's objective is to check the unexplored causal links between electricity generation and GDP growth in China by using a multivariate framework that contains technologies in the production or processing of goods, gross capital formation, labour force, financial sector development, and primary energy consumption as supplementary factors in growth model using the updated data. Applying new advances in time series data analysis, the study used wavelet coherence and hybrid non-parametric quantile causality approaches to analyze whether these factors predict GDP growth. We observed that technologies, capital formation, labor force, energy consumption, financial sector development, and electricity generation propel GDP growth in China. The quantile causality in mean and variance also corroborates that all these factors are causatives of GDP growth across their entire distribution. The Breitung-Candelon Spectral Granger-causality method further sustains the robustness of these results. Our findings provide valuable insight to policymakers to pave the way for these causative factors, mainly promoting electricity projects and adapting new technologies to propel economic growth in China.

Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations

This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.

Spatiotemporal Variability Analysis of Rainfall and Water Quality: Insights from Trend Analysis and Wavelet Coherence Approach

Spatiotemporal Variability Analysis of Rainfall and Water Quality: Insights from Trend Analysis and Wavelet Coherence Approach

Systemic risk-sharing between natural gas, oil, and stock markets in top energy producer and consumer countries

This study investigates the dependence and risk spillovers in the short-, medium- and long-terms between natural gas (NG), crude oil and stock markets of top energy producer countries (Russia, Ukraine, and US) and consumer countries (China, India, and EU members). Using the wavelet coherence approach, the results show a long-term dependence between NG and all stock markets between 2006 and 2022. Low and intermediate frequency co-movements of oil with stock markets are more pronounced among top energy producers than top energy consumers (except the EU). Further, there is heterogeneous dependence between stock markets and both NG and oil. Moreover, the dependence is positive and high for oil, and negative for NG, irrespective of the time horizon. NG is a hedge asset in the short-, medium- and long-terms for investors in top energy producer and consumer countries. Oil is a diversifier asset in the long term and a weak hedge asset in both short- and long-terms. We find evidence of significant bidirectional and asymmetric spillovers of NG, oil, and stock markets. The upside and downside risk spillovers for both NG and oil are higher in the long term than in the short- and medium-terms. NG sends more risk to stock markets than oil does in the long term, particularly in Russia. Our findings have significant implications for financial risk assessments and stock market stability.

Assessing the variability in chlorophyll-a and covariates in Arabian Sea using ARIMAX model and wavelet coherence approaches

The Arabian Sea is one of the most productive regions in the global ocean. In the present study, relationship of Chl-a with environmental parameters have been studied in the Arabian Sea. The Autoregressive Integrated Moving Average with explanatory variable (ARIMAX) models namely ARIMAX (1,0,1), ARIMAX (2,0,3), ARIMAX (5,0,4), ARIMAX (7,0,6), ARIMAX (8,0,8), ARIMAX (11,0,9) and ARIMAX (12,0,11) were tested. After evaluation, ARIMAX (2,0,3) and ARIMAX (12,0,11) were identified as the best fit models and can be used to model Chl-a in the Arabian Sea. Wavelet coherence approach was applied to understand the relationship of Chl-a with rainfall, sensible heat flux, remote sensing at 443 nm, aerosol optical depth, black carbon deposition and calcite concentration. Two strips of high correlation in the frequency bands of 4–8 and 8–16 significant at 95% level were observed in each wavelet coherence diagram except for remote sensing reflectance at 443 nm and sensible heat flux.

Revisiting the economic growth in the shadow of financial stress in times of crisis: evidence from FIGARCH and wavelet coherence approach

PurposeThis study empirically investigates the interdependency of select Asian emerging economies along with the financial stress index during the times of the global financial crisis, the Euro crisis and the COVID-19 period. Moreover, it inspects the long-memory effects of the different crises during the study period.Design/methodology/approachTo address the objectives of the study, the authors apply different statistical tools, namely the adjusted correlation coefficient, fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and wavelet coherence model, along with descriptive statistics.FindingsFinancial stress is having a prodigious effect on the economic growth of select economies. From the data analysis, it is found that the long-memory effect is noted in the gross domestic product (GDP) for India and Korea only, which implies that the volatility in the GDP series for these two nations demonstrates persistence and dependency on previous values over a lengthy period.Originality/valueThe study is unique of its kind to consider multi-segments within the period of the study to get a clear idea about the effects of the financial stress index on select Asian emerging economies by applying different econometric tools.

How do climate policy uncertainty and renewable energy and clean technology stock prices co-move? evidence from Canada

AbstractThis work probes the dynamic co-movement between the Climate Policy Uncertainty Index (CPU) and the Renewable Energy and Clean Technology Index (RECT) employing the novel wavelet power spectrum (WPS) and wavelet coherence (WC) approaches for monthly data between 2013 and 2022. Using the wavelet approach enables us to observe the causality direction from both time and frequency dimensions and also to help detect the causal linkage in the short-medium and long-term horizons. This is the first study aiming to perform this relationship from both time and frequency dimensions. Remarkably, findings reveal that: i) CPU seems only volatile in 2019 and 2021 in the short run; (ii) there was significant volatility in the RECT in the short and long terms (SLT) between 2018 and 2022; (iii) RECT significantly caused the CPU between 2014 and 2018; iv) after 2019, CPU started to cause RECT in the short and medium terms (SMT).

Analysis of ground and space geoelectric field during geomagnetic storms over Fresno, CA USA: wavelet coherence approach

Analysis of ground and space geoelectric field during geomagnetic storms over Fresno, CA USA: wavelet coherence approach

Wavelet analysis of the foreign aid and economic growth nexus in Turkey

AbstractTurkey is one of the highest donors in the world, who also receives substantial amount of aids from various entities and countries. This paper aims to investigate the on going nexus between economic growth (EG) and foreign aid (AID) in Turkey; utilizing yearly data between 1960 and 2022 by employing wavelet analysis tools: namely the power spectrum, wavelet coherence, wavelet cohesion and Continuous Wavelet Transform (CWT) causality approach, which provides information of both long‐run and short‐run linkages for separate subperiods of time and frequencies. Our results reveal that; (a) the result of the power spectrum indicates that at different frequency and period, there is evidence of significant variation in EG and AID; (b) the outcome of the wavelet coherence confirms that there is comovement at varying frequencies and varying periods between EG and AID, with AID leading; (c) the outcome of the wavelet cohesion shows a positive/negative connection in the short and medium term, but a negative connection in the long term; (d) the result of the CWT causality approach confirmed the ‘feedback hypothesis’ at the aggregate level, in‐phase and out‐phase condition. As a result, policymakers should be more effective in supporting EG at high frequencies and only under the rule of the economic cycle.

Assessing Bitcoin, gold and gold-backed cryptocurrencies as safe havens for energy and agricultural commodities: insights from COVID-19, Russia–Ukraine conflict and SVB collapse

PurposeThis paper aims to investigate the safe haven feature of Bitcoin, gold and two gold-backed cryptocurrencies (DGX and PAXG) against energy and agricultural commodities (crude oil, natural gas and wheat) during the COVID-19 pandemic, the Russia–Ukraine conflict and the Silicon Valley Bank (SVB) collapse.Design/methodology/approachThe authors use the threshold GARCH (T-GARCH)-asymmetric dynamic conditional correlation (ADCC) model to evaluate the asymmetric dynamic conditional correlation between the return series and compare the diversifying, hedging and safe-haven ability of Bitcoin, gold and the two gold-backed cryptocurrencies (DGX and PAXG) against financial swings in the commodity market during the COVID-19 outbreak, the Russian–Ukrainian military conflict and SVB collapse. The authors also calculate the hedging ratios (HR) and hedging effectiveness index (HE). The authors finally use the wavelet coherence (WC) approach to check our results’ robustness and further investigate the impact of the three crises on the relationship between Bitcoin, gold gold-backed cryptocurrencies and commodities.FindingsThe results show that PAXG serves as a strong hedging instrument while gold, Bitcoin and DGX act as strong diversifiers during normal times. During crises, gold outperforms Bitcoin as a diversifier and a safe haven against commodities. Gold-backed cryptocurrencies also exhibit strong performance as diversifiers and safe havens. HR results indicate that Bitcoin and DGX are more cost-effective for commodities risk mitigation than gold and PAXG. In terms of hedging effectiveness, gold and PAXG emerge as the best hedging instruments for commodities, while DGX is considered the worst one. Bitcoin shows superior hedging against oil compared to wheat and gas risks. Moreover, the results of the WC approach confirm those of the T-GARCH-ADCC results in both the short and long run.Originality/valueThis paper provides a comprehensive analysis of the diversification ability of gold, Bitcoin and gold-backed cryptocurrencies during different crises (the COVID-19 pandemic, the Russia–Ukraine conflict and the SVB collapse). By taking into consideration gold-backed cryptocurrencies, the authors expand the understanding of safe havens beyond conventional assets.

Metallic minerals production and environmental sustainability in China: Insights using ARDL bounds testing and wavelet coherence approaches

China plays a prominent role in the production and consumption of metallic minerals. However, the mining and smelting processes associated with these minerals contribute significantly to the release of CO2 emissions. This study examines the long-run and causal effects of metallic minerals production, renewable energy consumption, GDP growth, financial development, and urbanization on CO2 emissions in China for the period 1977–2021, using the IPAT framework. This study utilizes various econometric methods, including the Bounds test of co-integration, autoregressive distribution lag (ARDL), wavelet coherence test (WTC), and gradual shift causality tests, to analyze the relationship between the variables. The bounds test demonstrates the cointegration among the variables. The long-run results indicate that metallic minerals production, GDP growth, and urbanization have a positive effect on CO2 emissions, while renewable energy consumption and financial development have a negative effect on CO2 emissions. Further, WTC indicates a significant relationship between CO2 emissions and independent variables. Moreover, causality tests indicate a one-way causal relationship between metallic minerals production, GDP growth, financial development, and urbanization with CO2 emissions. In addition, there is a two-way causal relationship between CO2 emissions and renewable energy consumption. To address the environmental consequences of CO2 emissions, China should implement measures such as metal recycling and the adoption of low-carbon and energy-efficient technologies in the mining and production of metals.

Time-frequency nexus between remittances, exchange rates, and growth in Vietnam

The significance of remittances to the Vietnamese economy necessitates investigating how they affect the value of the Vietnamese currency and other macroeconomic factors. Macroeconomic articles struggle to discover their impact on economic development, but measured remittances by migrant workers have recently soared. There is no academic study that has examined this phenomenon in Vietnam. This study uses wavelet frameworks to analyze the lead-lag nexus between exchange rates, remittances, and economic growth in Vietnam in time-frequency domains from 1995 to 2020. Overall, we find that: (i) remittances enhance economic growth in the short and medium run; (ii) exchange rates boost remittances in the short and medium run; (iii) exchange rates promote GDP in all frequency and time domains. Moreover, the partial wavelet coherence and multiple wavelet coherence frameworks also offered evidence supporting the wavelet coherence approach. More importantly, the outcomes of wavelet-based Granger causality unveil that there is two-way causality between the selected indicators, which means that all the indicators can predict each other at different frequencies. Our empirical results provide meaningful information for market participants and policymakers.

The impact of geopolitical risks, financial stress, economic policy uncertainty on African stock markets returns and volatilities: wavelet coherence analysis

PurposeThis study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.Design/methodology/approachThis study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.FindingsThe study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.Originality/valueThis study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

Can ESG Stocks Be a Safe Haven during Global Crises? Evidence from the COVID-19 Pandemic and the Russia-Ukraine War with Time-Frequency Wavelet Analysis

In times of intense economic variability and social turbulence worldwide, this paper aims to examine the existence of transient correlations and interdependencies between the most important MSCI ESG indices worldwide and the most important commodities’ index, economic uncertainty, natural gas, gold, and VIX, in a geographical and social context during two recent crises: the COVID-19 pandemic and the energy crisis due to the Ukrainian war. Using daily data from 3 January 2020 and extending until 23 August 2022, this study applies a wavelet coherence approach to analyze time series co-movements, in order to emphasize all possible combinations’ correlations and achieve more accurate outcomes at any given time and frequency band simultaneously and spontaneously. The results show robust coherence between different geographical areas, time, and frequency bands, indicating both positive and negative correlations with most of the combined ESG indices and other economic indicators. The study suggests that stock indices of leading ESG companies in North America and Europe constitute a safe investment haven during major upheavals and crises, providing a way for investors to manage risk and generate positive returns while contributing to economic sustainability.

Connectedness and co-movement between dirty energy, clean energy and global COVOL

This paper examines dynamic connectedness and co-movement between dirty energy, clean energy and the global COVOL measure of Engle and Campos-Martins (2023) between January 2015 and May 2023 based on aggregated connectedness and wavelet coherence approaches. Our results show that COVOL and dirty energy are more closely linked in times of COVID-19 and geopolitical instability, while clean energy is more isolated from geopolitical events. Moreover, the coherence between dirty and clean energy markets is weaker after 2020, consistent with a decoupling hypothesis between dirty energy and clean energy.

Wavelet Coherence Analysis of Plasma Beta, Alfven Mach Number, and Magnetosonic Mach Number during Different Geomagnetic Storms.

We study the variation in plasma beta, Alfven Mach number, and magnetosonic Mach number during different geomagnetic storms of solar cycles 23, 24, and 25. In addition, we employ measurements of the solar wind's flow pressure, proton density, interplanetary magnetic field (IMF) along the z-direction (Bz), temperature, velocity, and geomagnetic index SYM-H. Here, the wavelet coherence (WTC) approach of plasma beta, the Alfven Mach number, and the magnetosonic Mach number have been used with the symmetrical H component (SYM-H) index, which are critical indicators of the plasma behavior and magnetic field interactions. A solar CME or, much less severely, a corotating interaction region (CIR), which is formed at the leading edge of a high-speed stream, is the source of the magnetic storm. The key objective of this study is to reveal the possible dependencies of the geomagnetic indices on whether a storm is driven by a CME or CIR. For CIR-associated storms, large amplitude waves occur preferentially with the rising Alfven Mach number and plasma beta. At the same time, the magnetosonic Mach number lacks variability during the storms caused by shock on the arrival of Earth's environment. This is different for CME-driven storms, where the variations of the magnetosonic Mach number do not show much fluctuation compared to the Alfven Mach number and plasma beta. WTC between SYM-H and our derived parameters indicates periodicities between 64 and 512 minutes and noticeable regions of significantly enhanced power on November 07-09, 2004, and June 21-23, 2015. However, the magnetosonic Mach number showed a noticeable coherence with SYM-H between 64 and 250 minutes on September 06-08, 2017. Although, during March 19-21, 2021, both the Alfven Mach number and magnetosonic Mach number showed a noticeable coherence with SYM-H, plasma beta showed none. These parameters can be used in the prediction of geomagnetic storms of the category above G3.

Time-varying Relationship between Fossil Fuel-Free Energy Indices and Economic Uncertainty: Global Evidence from Wavelet Coherence Approach

Considering the abysmal environmental impacts of anthropogenic activities, the entire world has been striving to shift toward a green economy. Also, the transition to a greener economy entails green financing, which escalates the pace and performance of environment-friendly economic activities. However, uncertain economic conditions might be a hindrance to green financing. Based on this, I investigate the co-movement of fossil-fuel-free energy equity indices returns and Twitter-based economic uncertainty using a wavelet coherence approach. The findings document that global, the US, and Japanese fossil-fuel-free indices show strong and positive co-movement with Twitter economic uncertainty for short- and medium-term investment horizons. However, a weak positive co-movement is observed during the Russia-Ukraine war. Based on these interesting findings, I suggest many policy implications.

Frequency spillovers between oil shocks and stock markets of top oil-producing and -consuming economies

Motivated by large oil price swings, high economic and geopolitical uncertainties, and the financialization of oil, this paper examines the frequency spillovers and co-movements between oil shocks (risk and demand) and the stock markets of top oil-producer and consumer countries, namely, Canada, China, Russia, Saudi Arabia, and the US. The analysis uses the time-domain spillover index of [1], the frequency-domain spillover of [2]; and the wavelet coherence approach. The findings reveal that spillovers run from the U.S., Canada, and, to a lesser extent, Russia to oil shocks. On the other hand, oil shocks, Saudi Arabia, and China constitute net receivers of shocks. The intensity of spillovers is heavier in the short-term frequency than in the intermediate- and long-term. Furthermore, the direction of spillovers is more defined in the long-term. The U.S. stock market exerts a strong impact on oil risk in general, but the impact is stronger in the short-term. Conversely, an oil demand shock is susceptible to innovations from Canada and Russia that are stronger in the long-term. This means that oil risk shock stemming from innovations in financial markets is short-lived and dissipates quickly due to quick reactions from market participants. On the contrary, long-term links characterize the relationship between oil demand shock and financial markets, mirroring the macroeconomic nature of the linkages. Finally, while the 2008 crisis, EDC, oil price crash, and the COVID-19 pandemic coincided with strong spillovers in the short-term, the COVID-19 era was marked by higher spillovers in the long term. The findings provide important information for investors and policymakers in terms of diversification, risk management, and efforts to mitigate contagion.