Wavelet Coherence Analysis Research Articles

Navigating the complexities of GCC real state markets: An analysis of interlinkages amidst shocks and oil effects

The study investigates the interconnectedness of GCC real estate markets using wavelet coherence analysis of daily data from 2013 to 2023 and analyzes the impact of various external shocks (political, economic, regulatory, and pandemic) and oil dependence on these dynamics. Despite shared cultural ties and energy intensity, findings reveal primarily weak to moderate linkages, with increased coherence observed during significant events. These linkages exhibit temporal variability, characterized by short-term spikes, loose medium-term correlations, and evolving long-term relationships. Isolating the effect of oil prices yielded limited impact, suggesting the influence of country-specific factors. This research offers valuable insights for policymakers, investors, and stakeholders, aiding informed decision-making during sensitive periods and fostering a deeper understanding of international finance and investments.

Multivariate and scale-dependent controls of deep soil carbon after afforestation in a typical loess-covered region

Afforestation is beneficial to improving soil carbon pools. However, due to the lack of deep databases, the variations in soil carbon and the combined effects of multiple factors after afforestation have yet to be adequately explored in >1 m deep soils, especially in areas with deep-rooted plants and thick vadose zones. This study examined the multivariate controls of soil organic carbon (SOC) and inorganic carbon (SIC) in 0–18 m deep under farmland, grassland, willow, and poplar in loess deposits. The novelty of this study is that the factors concurrently affecting deep soil carbon were investigated by multiwavelet coherence and structural equation models. On average, the SOC density (53.1 ± 5.0 kg m−2) was only 12% of SIC density (425.4 ± 13.8 kg m−2), with depth-dependent variations under different land use types. In the soil profiles, the variations in SOC were more obvious in the 0–6 m layer, while SIC variations were mainly observed in the 6–12 m layer. Compared with farmland (SOC: 17.0 kg m−2; SIC: 122.9 kg m−2), the plantation of deciduous poplar (SOC: 28.5 kg m−2; SIC: 144.2 kg m−2) increased the SOC and SIC density within the 0–6 m layer (p < 0.05), but grassland and evergreen willow impacted SOC and SIC density insignificantly. The wavelet coherence analysis showed that, at the large scale (>4 m), SOC and SIC intensities were affected by total nitrogen-magnetic susceptibility and magnetic susceptibility-water content, respectively. The structural equation model further identified that SOC density was directly controlled by total nitrogen (path coefficient = 0.64) and indirectly affected by magnetic susceptibility (path coefficient = 0.36). Further, SOC stimulated the SIC deposition by improving water conservation and electrical conductivity. This study provides new insights into afforestation-induced deep carbon cycles, which have crucial implications for forest management and enhancing ecosystem sustainability in arid regions.

US Trade Policy Uncertainty and Nigeria’s Participation in Global Value Chains: Evidence from Frequency Domain Causality, Wavelet Coherence Analysis, and Time-Varying Causality

AbstractThis study examines for the first time whether trade policy uncertainty emanating from the United States significantly predicts Nigeria’s participation in global value chains. It further examines the kind of global value chain participation in Nigeria (forward or backward linkages) that is most predictable by the United States trade policy uncertainty. To achieve the study objective, frequency domain causality, wavelet coherence analysis, and time-varying causality are employed in analyzing annual data sets over the period 1990–2019. The summary of the study findings is as follows; first, trade policy uncertainty emanating from the United States significantly predicts Nigeria’s overall integration into global value chains. Second, there is a positive association between Nigeria’s integration into global value chains and the United States trade policy uncertainty. Third, Nigeria’s overall integration into global value chains is highly correlated with the United States trade policy uncertainty in periods around the global economic crisis of 2007/08 and Nigeria’s recession of the late 2010s. Fourth, most of the predictability noticed in the relations between trade policy uncertainty generated by the United States and the overall global value chain participation is caused by the relations between the United States trade policy uncertainty and Nigeria’s forward linkage into global value chain participation.

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.

How does green investment, financial inclusion, and digitalization drive environmental sustainability in China? A perspective based on quantile-on-quantile regression and wavelet coherence analysis

How does green investment, financial inclusion, and digitalization drive environmental sustainability in China? A perspective based on quantile-on-quantile regression and wavelet coherence analysis

Time-varying Relationship between Oil Prices, Stock Market Performance, and Covid-19 in United States of America, China, and Malaysia: Evidence from Wavelet Approach

The purpose of this paper is to have a better understanding of the reactions to the oil price and stock market performance over a different period of the Covid-19 pandemic from January 2019 to August 2022 (Pre Covid, During Covid, and Post Covid) in the United States of America, China, and Malaysia. In the study, various approaches such as line graphs, descriptive statistics, correlation, wavelet correlation, continuous wavelet transform, and wavelet coherence analysis were used to analyse the decomposition of time series oil and stock market data in different time period. The study provides evidence of positive linear relationships between crude oil prices with the 3 countries’ stock markets in pre-covid, during-Covid and post-covid. In addition, both oil price and stock prices have evidence of a lead-lag relationship with each other, which differs in the period of time. The research results will benefit investors for better prediction during future pandemic situations and economic crises.

Is geopolitical oil price uncertainty forcing the world to use energy more efficiently? Evidence from advanced statistical methods

This paper argues that energy efficiency is a potent shield against oil price uncertainty in an increasingly interconnected world fraught with geopolitical tensions. By reducing dependence on oil, enhancing economic resilience, and improving energy security, energy efficiency measures offer multifaceted benefits for both national economies and global stability. Specifically, a wavelet coherence analysis is conducted to study the response of global energy efficiency to geopolitical oil price uncertainty. Quantile-on-quantile and quantile regressions are additionally employed to separate the impacts of various geopolitical oil price risk quantiles on the quantiles of energy efficiency. These methods are utilized in the examination of global time-series data covering the timeframe 2004:Q1–2020:Q4. The wavelet coherence outcomes indicate a positive correlation between geopolitical oil price uncertainty and energy efficiency, particularly during the energy crisis of the 2000s and the COVID-19 pandemic. The results also reveal that geopolitical oil price uncertainty leads to energy efficiency, indicating that an upsurge in geopolitical oil price uncertainty causes energy efficiency to increase. Moreover, the results from quantile-on-quantile and quantile regressions affirm the predominantly positive effects of geopolitical oil price uncertainty on global energy efficiency. Our conclusion therefore is that through strategic investments, innovative policies, and international collaborations relating to energy efficiency, nations can fortify themselves against the destabilizing effects of geopolitical conflicts on energy markets. This would ensure a more sustainable and secure energy future for all.

Credit Cultivation: A deep dive into the harmonies and dissonances of Macroeconomics impact on agricultural financing practices in Djibouti

In this study, we investigate the impact of selected macroeconomic factors on agricultural financing practices in Djibouti. Therefore, the objective of this study is to understand how these factors influence the accessibility, distribution, and effectiveness of financial resources within the agricultural sector. To achieve this objective, we employed Autoregressive Distributed Lag (ARDL) and Wavelet Coherence Analysis on an annual dataset covering a span of 25 years, ranging from 1998 to 2022. The findings reveal that factors such as inflation, diminishing rural population, economic growth, and the institutional framework of the country exert negative pressures on the availability of funding for agriculture. However, amidst these challenges, we find that official development assistance serves as a positive mitigating factor, offering crucial support to the agricultural sector. These findings hold significant implications for policymakers in Djibouti by providing valuable insights into how to develop targeted interventions to support farmers, increase access to credit, and improve agricultural productivity, as it lays the foundation for investments in food security, technology adoption, and climate-resilient practices.

Crude oil price volatility—An unintended consequence of carbon pricing: Evidence from transfer entropy and wavelet-partial wavelet coherence analyses

Crude oil price volatility—An unintended consequence of carbon pricing: Evidence from transfer entropy and wavelet-partial wavelet coherence analyses

The degree of Asian-European markets connectedness: examining the impact of global disorders using a spectral analysis

PurposeThis paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency.Design/methodology/approachThe empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain.FindingsThe results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish.Originality/valueThese results have crucial implications for investors regarding the benefits of international portfolio diversification.

Examining the income gap-gender inequality nexus in Africa using bi-wavelet and partial wavelet coherence analysis

The eradication of gender discrimination traditions and the reduction of income disparities are vital global objectives outlined in Sustainable Development Goals 4 and 10, particularly in African nations where gender and income gaps persist. In alignment with these goals, this study employs wavelet techniques to investigate the relationship between income inequality and gender equality in 31 selected African countries, utilizing available data spanning from 1998 to 2020. Analysis of the co-movement between gender equality and income gap reveals that gender equality plays a crucial role in narrowing the income disparity in African countries. Expanding the analysis to control for the impact of economic growth through the implementation of partial wavelet tools, does not change these results. Further dissemination of our findings reveals that improved gender equality has a more pronounced impact on reducing income inequality in anglophone nations compared to their francophone counterparts, given variations in legal frameworks and religious practices. This emphasizes the importance of gender-sensitive policies in effectively addressing income inequality in Africa and suggests that policymakers should tailor country-specific policies to account for differences in gender laws, cultures, and religious beliefs. Overall, our study presents novel evidence linking income and gender inequality in Africa at country level.

Covid-19 vaccination and stock markets volatility around the world. Evidence from wavelet analysis

ABSTRACT The impact of COVID-19 vaccination on stock markets volatility has been the focus of academics in the aftermath of the pandemic. We use wavelet coherence analysis on the daily stock markets for 74 countries, covering all regions of the world to test relationship between vaccination and stock markets. Our results show the positive effect of vaccination against COVID-19 for all stock markets, while the effects are stronger for developed countries and weaker for emerging countries. Furthermore, effect is present in short-medium term for emerging economies, while for developed countries the effect is present also in the long-term.

Impacts of meteorological conditions on the plummeting surface-reaching solar radiation over a sub-tropical station – Pune, India

The unprecedented rise of solar energy as a dominant power source is a remarkable feat, considering our historical struggle to establish its widespread adoption. However, the dynamics of solar radiation reaching the Earth's surface are greatly influenced by meteorological conditions such as clouds, aerosols, and wind speed. This study investigates the detailed changes in surface-reaching solar radiation within the subtropical region Pune from 1993 to 2022. By employing analytical methodologies including linear regression, wavelet coherence, and quantitative analysis, our study reveals a compelling and alarming pattern of plummeting solar radiation over Pune (also known as solar dimming). The total drop is ∼1.04 Wm−2 year−1 (0.21 % year−1), with a more steep decline registered from 2018 to 2022, providing an astonishing rate of ∼6.01 Wm−2 year−1 (1.21 % year−1). Moreover, wavelet coherence transform revealed an antiphase coherence between solar radiation and both clouds and aerosols, suggesting their substantial influence over a longer timeframe. Extensive analysis utilizing observational data is conducted to find the contributing elements to the significant decline during 2018 to June 2022. Analysis of solar radiation statistics showed that clouds led to a substantial reduction of ∼30.18 % compared to conditions with clear-skies. Similarly, the aerosols have a comparable effect, reducing radiation by ∼17.75 % compared to days characterized by low or negligible aerosol loading for the same period. Furthermore, a through statistical analysis investigates a notable anticorrelation (ρ = −0.64) between wind speed and solar radiation. These findings underscore the imperative of incorporating climatic variables while studying solar dimming trends and devising sustainable renewable energy strategies to ensure the long-term viability of solar energy source.

Climate Risk and Sustainable Investment in Asia

This study conducts an empirical analysis of the impact of climate risk on Environmental, Social, and Governance (ESG) investments in Asia. Using monthly data from 2015 to 2022 for 11 Asian countries and applying wavelet coherence analysis with a panel Auto-regressive Distributed Lag Model (ARDL) cointegration approach, the study concludes that (1) ESG and stock market returns are positively correlated during economic turmoil in China and Malaysia; (2) climate risk positively influences ESG returns, suggesting that it enhances returns during uncertain times rather than having a detrimental effect; (3) returns on conventional assets are negatively impacted by climate risk; and (4) ESG investments serve as an effective hedge against climate risk and provide a sound strategy for portfolio diversification in the Asian market.

COVID-19, the Russia–Ukraine war and the connectedness between the U.S. and Chinese agricultural futures markets

This study focuses on how recent global crises such as the COVID-19 pandemic and the Russia–Ukraine war have affected the relationship between the U.S. and Chinese agricultural futures markets. By applying wavelet coherence analysis (WCA) and time-varying parameter vector autoregression (TVP-VAR), we obtain the following findings. First, both events have changed the correlation and lead–lag comovement between U.S. and Chinese soybean and corn futures returns but have little impact on the comovement between the two cotton futures returns. Second, U.S. agricultural markets transmit more volatility risk to Chinese markets than the risk spillover from the reverse direction. Third, the risk spillover enhancement effect from the war is stronger than that from the pandemic, which is obvious in both the soybean and corn futures markets but not in the cotton market. Our paper has implications for policy makers seeking to stabilize agricultural commodity prices during global crisis episodes and for designing strategies for cross-market hedging of spillover risks among commodity markets for international investors.

The nexus between inflation and financial integration in the euro area

ABSTRACT This paper employs wavelet coherence analysis to examine the co-movement of inflation and financial integration (FI) in the Euro area. We measure FI using the ECB’s integration indicators. Our results demonstrate that the relationship between inflation and FI varies over time, presenting a dynamic nature. The link between inflation and FI intensifies during crisis. We find a limited impact of inflation on money market integration while documenting a significant adverse effect on bond and equity market integration.

Does foreign capital flow into ‘greener pastures’? Exploring the potential of FDI in mitigating carbon emissions in African states

ABSTRACT Our study provides fresh insights into FDI's impact on carbon emissions across 51 African nations. Analysing data from 1990 to 2021, we use ARDL estimates and wavelet coherence analysis. While ARDL shows insignificant cointegration for most countries, wavelet coherence reveals: (i) 17 countries with a positive FDI-emissions link, (ii) 16 countries with a negative association, and (iii) 18 countries with ‘sign switching’ nonlinear dynamics. With no clear regional patterns, our main policy implication stresses the need for aligning regional efforts, like AfCFTA, with tailored country-specific strategies to guide capital flows towards cleaner industries. We advocate for industry-level FDI data in Africa to pinpoint sectors needing monitoring for greener production. On the global policymaking level, we assert that FDI cannot significantly address climate justice for most African countries.

Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task.

Approximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features. For our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups. The number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%. The dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts.

Multiscale association between large‐scale climate variability modes and drought in India via wavelet analysis

AbstractExtreme events, such as droughts, are influenced by climate variability modes such as El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multi‐decadal oscillation (AMO) and Equatorial Indian Ocean Oscillation (EQUINOO). Due to the significant interdependency among the climatic indices at various frequencies, a time‐frequency framework is necessary to better understand the teleconnection. The present study evaluates the association between climate variability modes and drought index in India using different variants of wavelet analysis such as wavelet coherence analysis (WCA), partial wavelet coherence analysis (PWCA), multiple wavelet coherence analysis (MWCA) and wavelet reconstruction methods. In this study, four major climatic indices (ENSO, PDO, AMO, EQUINOO) and the drought index, standard precipitation index (SPI), at four different monthly time periods (1, 3, 6 and 12) are considered. The results from the WCA analysis highlight significant teleconnection spectra for ENSO, PDO and EQUINOO at 2–8, 8–16 and 16–32 year time‐frequency bands. Further, PWCA results reveal significant interdependency in the teleconnection of ENSO and PDO at all the scales, while ENSO and EQUINOO are found to be independent modes of interannual drought variability in India. Finally, MWCA results show that the combination of EQUINOO and ENSO better explains the interannual variability of droughts in Northwest and Central northeast India. The results of this study will help improve drought prediction and early warning systems in India, by selection of appropriate climatic oscillations for different regions in India.

Time Frequency and Co-movements between Global Economic Policy Uncertainty, Precious Metals and Agricultural Prices: A Wavelet Coherence Analysis and Bootstrap Rolling Window Granger Causality

Examining the links between economic uncertainty and precious metals allows us to explore the interconnectedness of various economic factors and their potential impact on markets, investments, and global trade. Precious metals such as gold and silver have historically been used as safe havens during times of economic uncertainty. Understanding how these metals interact with fluctuating economic policies can help assess their role as a safe haven and their importance to investors. Analyzing the links between precious metals, economic uncertainty, and agricultural prices can help policymakers design measures to mitigate negative impacts on the economy and foster financial and food stability. The main objective of this paper is to examine the co-movements between global economic policy uncertainty and each commodity such as oil, precious metals and agricultural prices. We employ two empirical approaches such as wavelet coherence and bootstrap rolling window Granger causality. The dynamic causality according to the bivariate framework between variables is analyzed. We used monthly data during the period span from January 2004 to September 2022. Empirical results indicate the evidence of unidirectional, bidirectional and absence of causality between variables. In addition the co-movements between GEPU and each variable are positive and negative.