Wavelet Coherence Approach Research Articles

A LINKAGE BETWEEN PRODUCER AND CONSUMER PRICES IN EUROPEAN COUNTRIES

This paper examines a linkage between consumer and producer prices within European countries. The research sample in this paper includes 23 European countries and monthly data between February 2000 and March 2023 for the harmonized index of consumer prices (HICP) and producer prices index (PPI). Using a wavelet coherence approach country country-specific linkage was provided for each of the considered countries. Empirical findings suggested diverse relationships. However, the most frequently observed linkage is the one with producer prices as leading and consumer prices as lagging variables and a positive correlation between the two. Furthermore, the relation is more prominent at higher scales or lower frequencies. Conclusively, monetary policy needs to monitor producer prices while targeting inflation rates.

Whether and when did bitcoin sentiment matter for investors? Before and during the COVID-19 pandemic

Using a wavelet coherence approach, this study investigates the relationship between Bitcoin return and Bitcoin-specific sentiment from January 1, 2016 to June 30, 2021, covering the COVID-19 pandemic period. The results reveal that before the pandemic, sentiment positively drove prices, especially for relatively higher frequencies (2–18 weeks). During the pandemic, the relationship was still positive, but interestingly, the lead-lag relationship disappeared. Employing partial wavelet tools, we factor out the number of COVID-19 cases and deaths and the Equity Market Volatility Infectious Disease Tracker index to observe the direct relationship between a change in sentiment and return. Our results robustly reveal that, before the pandemic, sentiment had a positive effect on return. Although positive coherence still existed during the pandemic, the lead-lag relationship disappeared again. Thus, the causal relationship that states that sentiment leads to return can only be integrated into short-term trading strategies (up to six weeks frequency).

Transition towards environmental sustainability through financial inclusion, and digitalization in China: Evidence from novel quantile-on-quantile regression and wavelet coherence approach

This research investigates the potential for promoting environmental sustainability in China by analyzing the relationship between financial inclusion, digitalization, and environmental sustainability. Utilizing a multifaceted methodology consisting of quantile-on-quantile regression, Granger causality in quantiles, and wavelet analysis, supplemented by robustness tests, the research reveals a strong positive correlation between financial inclusion, digitalization, and environmental sustainability in China. The findings underscore the pivotal roles of technological innovation and the financial sector in steering the nation towards sustainable development. Essentially, the study indicates that embracing financial inclusion and advanced technology is vital for reinforcing the environmental sustainability of China's economic growth. These insights are crucial for emerging economies aiming to align economic growth with sustainable development objectives through technology innovation and enhanced financial accessibility.

Examining the asymmetric effects of fossil fuel consumption, foreign direct investment, and globalization on ecological footprint in Mexico

AbstractThis paper investigates the asymmetric effects of fossil fuels, foreign direct investment (FDI), and globalization on the ecological footprint (EFP) in Mexico from 1975 to 2020. To this end, the Autoregressive Distributed Lag (ARDL), Nonlinear Autoregressive Distributed Lag (NARDL), and the wavelet coherence approach are conducted. Based on the outcomes of the ARDL method, economic growth and fossil fuel consumption lead to ecological degradation, while foreign direct investment improves environmental conditions. Globalization has no impact on the environment. The NARDL approach illustrates that a positive shock to fossil fuels, FDI, and globalization degrades the environment. The wavelet coherence results emphasize the adverse environmental influence of economic growth and fossil fuels. These results imply that the Mexican government should conduct its economic expansion considering the principle of sustainable development. In this context, policymakers should propagate carbon taxes and similar instruments to promote clean energy sources that can replace fossil fuels and are compatible with sustainable development policies.

Fostering environmental sustainability: An analysis of green investment and digital financial inclusion in China using quantile-on-quantile regression and wavelet coherence approach

This study explores the opportunities for enhancing environmental sustainability in China through a meticulous examination of the interrelations among green investment, digital financial inclusion, and environmental sustainability. An innovative tripartite methodology encompassing quantile-on-quantile regression, Granger causality in quantiles, and wavelet coherence analysis, complemented by extensive robustness checks is employed to unveil nuanced insights. The findings reveal a robust positive interrelation between green investment, digital financial inclusion, and environmental sustainability, notably within middle to upper quantiles of environmental sustainability, extending across varying levels of digital financial inclusion and environmental sustainability quantiles (0.05–0.95). This synergy is pivotal in middle to high quantiles of both green investment and digital financial inclusion, signifying the critical role of each in environmental conservation. The research accentuates the imperative of strategic investments in eco-friendly innovations and the financial sector to usher China towards a sustainable developmental paradigm. Overall, the findings suggest that green investment, financial inclusion, and play a critical role in enhancing environmental sustainability of China's current economic growth. This study provides valuable insights for developing countries seeking to achieve sustainable development goals through leveraging green investment, and digital financial inclusion.

Dependency and causal relationship between ‘Bitcoin’ and financial asset classes: A Bayesian network approach

AbstractThis study employs the Bayesian Networks (BN) and the wavelet coherence approaches to invest the relationship between Bitcoin volatility and financial asset classes (MSCI world equity index, S&P Goldman Sachs Commodity Index [GSCI], US index and Investment Grade Corporate Bond Index ETF [PIMCO]) using daily data for the period from August 2011 to October 2021. The results show that the causal relationship between Bitcoin and other financial assets varies depending on the market states. During the low volatility periods, Bitcoin has a stronger impact on the GSCI, while during the stability periods, it has a direct effect on the US index and the MSCI world index. In contrast, during high volatility periods, Bitcoin has a direct impact on both the GSCI and PIMCO indices. The key findings enabled us to provide implications for US investors to promote asset allocation and risk management covering both Bitcoin and traditional financial markets. The results suggest that policymakers should watch Botcoin closely to preserve financial stability.

Are markets sentiment driving the price bubbles in the virtual?

This paper investigates the existence of speculative bubbles in four crypto-market components (Bitcoin, Ethereum, CRIX index, DeFi pulse index) while date-stamping them, before using a wavelet coherence approach to look after the co-movement of each of those four digital tokens with three sentiment indices reflecting three different markets (Stock, gold, and crypto markets). Thereafter, we study the sentiment-price return connection and its concordance with periods of existing bubbles. Our results show the existence of multiple bubble regimes in the virtual market and more specifically, a huge bubble occurring during the crypto-market bull phase for Bitcoin, Ethereum, and CRIX. Moreover, we find that the bubbles that occurred during the COVID-19 pandemic concord with periods of high sentiment co-movement with the three markets. Hence, some of those bubble periods are in phases with periods of high co-movement with sentiments toward different markets. In addition, the fear and greed toward the crypto-market seem to be the leading sentiment index that highly influences the crypto-market components and their bubble appearances.

Impact of COVID-19 and lockdown stringency on foreign institutional investment in India: evidence from wavelet coherence and spectral causality approaches

Impact of COVID-19 and lockdown stringency on foreign institutional investment in India: evidence from wavelet coherence and spectral causality approaches

Revisiting the connectedness between oil prices and uncertainty indicators in BRICS countries

The paper revisited the connectedness between oil prices (ROILP), economic policy uncertainty (EPU), and geopolitical risks (GPR) in the BRICS countries using data from January 1997 to October 2022. The study applied the spillover index, a rolling window based on the generalized VAR framework, and a wavelet coherence approach to investigate the passthrough, multiple regime changes, co-movements, and lead-lag status between the series. The study found that oil prices were a significant transmitter of shocks to other variables in the system among countries, with Brazil having the highest spillover index (11.7%) followed by South Africa (10.5%), India (10.2%), China (9.5%), and Russia (9.2%). The rolling spillover from GPR to ROILP suggested that GPR had a varying spillover effect on real oil prices in the BRICS economies, with Russia and India experiencing relatively stronger effects compared to the other countries. The spillover from EPU to ROILP showed that Brazil and China recorded significant increases in spillover effects, both respectively reached peaks of 54% and 61% suggesting their vulnerability to EPU and its impact on real oil prices during economic stress. The wavelet coherence largely revealed mixed directions of co-movement and heterogeneity between ROILP and EPU (GPR) across countries, indicating that the relationship between the variables was not consistent, varied over time and scale. Different from previous studies, the analysis focused on the direction of the spillover between oil prices and uncertainty measures, then estimated the numerical amount of the spillover to discover the variable that gave more shock than it received. While the wavelet coherence identified variations in coherence, across the time and frequency domain, the adoption of the rolling-window technique enabled the capture of the influence of arbitrary multiple regime changes in the spillover. The findings had some policy implications.

Multiscale interdependence between economic policy uncertainty and industrial production of Central and Eastern European countries

AbstractThis paper shows how the three economic policy uncertainties (EPUs), namely global economic uncertainty, US economic policy uncertainty and German economic policy uncertainty, impact the industrial production of 8 Central and Eastern European countries. The investigation is done in both time and frequency domains, using the wavelet coherence and wavelet correlation approaches. The US EPU has the strongest effect on all industrial productions, while this effect was recorded during the global financial crisis and the corona virus pandemic. The most intense effect was found in the time-horizon between 4 and 8 months. The Czech Republic and Hungary suffer the strongest impact from EPU, probably because these two economies have relatively high ratio of export (import) vis-à-vis GDP. We fail to find very strong and wide areas of coherence in the Slovakian plot, although Slovakia has the highest level of export (import) to GDP. However, the wavelet correlation findings indicate that Slovakia has relatively high negative correlation at third wavelet scale, which is perfectly in compliance with its high export (import) share in GDP.

Tourism, transport energy consumption, and the carbon dioxide emission nexus for the USA: Evidence from wavelet coherence and spectral causality approaches

The aim of this study is to analyze the dynamic relationship between tourism, transport energy consumption, and carbon dioxide emissions in the United States from the 1st quarter of 1995 to the 4th quarter of 2019. To this end, we utilize the autoregressive distributed lag (ARDL), the Wavelet Coherence Approach (WCA), and the Breitung-Candelon spectral granger causality approaches. The empirical outcomes confirm that the variables included in the model exhibit cointegration. The estimations of the wavelet coherence approach confirm that tourism stimulates transport energy consumption, whereas both tourism and energy consumption bolster carbon emissions in the United States. The outcomes for the Breitung-Candelon spectral granger causality approach suggest that our variables exhibit causal associations at various frequencies. These are findings are also robust to alternative econometrics specifications. These empirical outcomes underscore the fact that tourism propel both transport energy consumption and carbon emissions. Our study helps policymakers in regards to revisiting the role of tourism and transport energy consumption concerning emissions in order to cope with environmental challenges in the United States.

The impact of natural disaster shocks on business confidence level and Istanbul Stock Exchange: A wavelet coherence approach

The focus of this study is to examine the short‐ and long‐term causal effects of natural disaster shocks on the stock market and business confidence level in the time‐frequency domain by studying the co‐movements between earthquake data and the Istanbul Stock Index closing prices, using the Wavelet Coherence and Phase Difference analyses. The empirical findings reveal that the relationship between earthquake events and financial markets is not stable over time and across different time horizons. The linkage becomes stronger in the long term when the impact of the earthquake event coincides with a financial crisis reflecting a combined effect. This nexus is also strong during the years 2007–2011 reflecting a similar combined effect of both an earthquake event and the global financial crisis. Meanwhile, co‐movement between the earthquake and the financial market index implies a negative effect in the period 2011–2012, indicating short‐run effects of stock market shocks. Differentiating these short‐term and long‐term effects has implications for risk management and policymaking.

Environmental Regulations and Carbon Emissions: The Role of Renewable Energy Research and Development Expenditures

This present research offers fresh insights regarding the causality nexus between environmental regulations and a sustainable environment. Also, this study considered the importance of renewable energy research and development (RERD), technological innovation (TI), and economic growth (GDP). Using the U.S. extended dataset covering the period 1990–2020; this research employed the wavelet methods (wavelet power spectrum and wavelet coherence) to observe the causal connections between mentioned variables based on the time-frequency domain. The empirical results from the wavelet power spectrum asserted that carbon emissions (CO2), RERD, and TI are vulnerable during the study period, while GDP and environmental policies are stable. Additionally, the wavelet coherence approach unveils relationships both in-phase and anti-phase. A causal connection is evident between CO2 and other variables. Particularly, a unidirectional causality is found from TI to CO2 emissions, while a bidirectional causal association exists between GDP and CO2, and environmental policy stringency, and CO2. Moreover, a bidirectional causality exists between RERD and CO2, but this association is insignificant. Based on findings, this research suggests enhancing RERD investment, strengthening environmental regulations, and promoting green technological innovation to attain a sustainable environment.

Spatial evaluation and zoning strategy of land use elemental conflicts in heavy industrial zones: evidence from central Liaoning Province in Northeast China.

The matching imbalance of resource factors leads to land use elemental conflicts (LUECs), which has become the bottleneck restricting high-quality social and economic development. The heavy industrial zones (HIZ) have become the focus area of LUECs due to the high-resource consumption. Taking the urban group of central Liaoning Province, the area of industrial revitalization in northeast China as a case study area, the study proposed a wavelet coherence approach to identifying the influencing indicators and indicators weight of LUECs for spatial evaluation. Two-dimensional graph theory is used to cluster the evaluation results of LUECs at the plot scale and controls the main indicators to put forward the zoning strategies of LUECs. The results showed that the main indicators affecting LUECs in the western part of the HIZ are mainly human indicators, while the fierce conflicts in the east mainly come from natural indicators. The zoning strategies of LUECs in the HIZ should prevent excessive energy consumption from increasing carbon emissions in intense conflict zone and moderate conflict zone and strengthen the rural settlement arrangement and soil erosion control in mild conflict zone and structure ecological security early warnings in potential conflict zone. This study provides an important reference for land use conflicts in the global heavy industrial urban agglomeration.

Nexus between Energy Consumption, Foreign Direct Investment, Oil Prices, Economic Growth, and Carbon Emissions in Italy: Fresh Evidence from Autoregressive Distributed Lag and Wavelet Coherence Approach

The aim of this study is to explore the impact of economic growth (GDP), energy consumption, foreign direct investment, oil price, and exports on carbon emissions by employing yearly time series data for Italy for the period 1971–2019. For this purpose, we employed the autoregressive distributed lag (ARDL) model and wavelet coherence approach to analyze the interconnections among variables. The cointegration results confirm the long-run association between our variables. Our findings show that GDP has a positive impact on carbon emissions, while the square of GDP has a negative impact, thus confirming the presence of the EKC hypothesis. Further, oil prices have a detrimental impact on carbon emissions both in the long- and short-term; on the contrary, foreign direct investment, energy consumption, and exports promote environmental degradation. We propose some important policy recommendations based on these findings to address the environmental constraints.

Did biofuel production strengthen the comovements between food and fuel prices? Evidence from ethanol-related markets in the United States

U.S. ethanol production from corn rapidly grew since the enactment of the Energy Policy Act of 2005 and the Energy Independence Act of 2007. Although many economists analyzed the relationships between food and fuel prices, the mechanisms between the price comovements and ethanol production have not yet been investigated with numerical models. We test the hypothesis that ethanol production strengthens the linkages between food and energy prices using the dynamic conditional correlations (DCC) -mixed-data sampling (MIDAS) model and wavelet coherence approach over the sample period 2005–2020. To the best of our knowledge, this research is the first to apply the combination of the DCC-MIDAS and wavelet methodologies to address the relevant issues. Our primary findings are as follows (1) the DCCs for the three pairs (i.e., ethanol-corn, crude oil-corn, and ethanol-crude oil): exhibited positive linkages in most of the sample period. (2) The ethanol-corn DCC positively and negatively affects ethanol production in the short and long run. (3) Ethanol production negatively and positively influenced the crude oil-corn DCC in the short and long term. The results suggest that U.S. biofuel policies foster the food-energy long-run linkages and enhance the probability of concurrent food and energy crises.

A wavelet coherence approach to zoning supply-demand matching of carbon sequestration service

Carbon sequestration (CS) is an important regulating service provided by natural ecosystem which plays an important role in mitigating global climate change. However, there is often spatial mismatch between the carbon sequestration supply and demand (CSSD), which makes it difficult to reduce carbon emissions and increase carbon sinks to achieve local carbon balance. Therefore, it is important to clarify the optimal scale to explore spatial matches and mismatches between CSSD and delimit spatial units for implementing effective carbon-focused management policies. Taking Hunan Province, China as an example, we evaluated CSSD in 2001 and 2017, and identified the optimal scale of spatial matching based on wavelet coherence analysis. The results showed that from 2001 to 2017, CS supply in Hunan Province increased by 6.45 %, while CS demand increased by 261.11 %. 8.40 km was identified as the optimal scale of CSSD spatial matches and mismatches, and Hunan Province could be divided into 3231 spatial units including four types according to the combination of CSSD, i.e. High supply-High demand, Low supply-Low demand, High supply-Low demand and Low supply-High demand. Based on the type changes of spatial units from 2001 to 2017, it was found that the key areas in need of ecological restoration were located in the east side of Xuefeng Mountains and the west side of Luoxiao Mountains, which could support accurate ecosystem monitoring and management under the background of improving the ‘one map’ of territorial space in Hunan Province. Based on wavelet coherence analysis, this study provided a spatial zoning approach for sustainable land use management, with a special focus on carbon sequestration supply and demand.

Analyzing the streamflow teleconnections of greater Pampa basin, Kerala, India using wavelet coherence

Climate change is one of the major global threats which affects the streamflow variability. In order to disentangle the relationships between climatic oscillations (COs) and streamflows (SF), this study presents a framework for the identification of main climatic factors integrating Bivariate wavelet coherence (BWC), Multiple wavelet coherence (MWC), and Partial wavelet coherence (PWC) approaches. The application of this method is demonstrated by analysing the monthly SF-CO teleconnections of four stations belonging to the rivers of Greater Pampa region of Kerala, which was severely affected by the 2018 Kerala floods. To investigate the impact of these large-scale climatic patterns on the SF of this region, COs, specifically El Niño Southern Oscillation, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), and North Atlantic Oscillation (NAO), are taken into consideration. The relationships between different SF-COs are statistically quantified with the help of Average Wavelet Coherence (AWC) and the Percentage of Significant Coherence (PoSC). The BWC analysis showed that the IOD is the dominant predictor for SF1 (AWC = 0.46, PoSC = 26.05%) and SF2 (AWC = 0.5, PoSC = 30.03%) stations while NAO is the dominant predictor for SF3 (AWC = 0.43, PoSC = 18.25%) and SF4 (AWC = 0.48, PoSC = 24.83%) stations. Addition of NAO to the most significant combination of SF-PDO-IOD from the two-factor analysis produced the highest coherence values for all the stations. The PWC analysis indicated a drastic reduction in coherence values with respect to the BWC analysis, indicating a strong interrelationship between different COs and SF.

Analysis of Dynamic Co-movements Between External Shocks, International Oil Prices, Money Supply and Inflation: Evidence from Wavelet Coherence Approach

Hem mal piyasasındaki hem de para piyasındaki aktiviteler Küreselleşme ile birlikte içsel ve dışsal olarak ekonomiyi etkileyebilecek şoklara daha duyarlı bir hale gelmiştir. Belirli dönemlerde yaşanan iktisadi buhranlarla birlikte ortaya çıkan ekonomi politika belirsizlikleri, ani talep artışları veya azalışları neticesinde ortaya çıkan ekonomik durgunluktan çıkmak için Merkez bankalarının uyguladıkları genişleyici para politikaları küresel ölçekte enflasyonu hızlandırmıştır. Dolayısıyla uluslararası emtia, gıda ve enerji fiyatlarında yukarı yönlü enflasyonist etkiler ortaya çıkmıştır. Ortaya çıkan bu durum neticesinde, dünya genelinde birçok ülke merkez bankasının enflasyonla mücadelede önlem oluşturacak politikaları devreye sokmasına neden olurken, küresel ekonomi politika belirsizliği, enerji fiyatları ve para miktarındaki sıkı ilşkinin enflasyonla mücadelede nasıl dengede tutulacağı sorusunu da gündeme getirmiştir. Bu çalışmada, Türkiye için Ocak 2006 ve Haziran 2022 dönemleri arasındaki aylık veriler kullanılarak küresel ekonomik politika belirsizliği, para arzı, uluslararası petrol fiyatları ve enflasyon arasındaki zaman ve frekans alanındaki dinamik etkileşimler analiz edilmiştir. Zaman-frekans uzayında analiz yapmamıza yardımcı olan çapraz dalgacık dönüşümü (cross wavelet transform) ve dalgacık tutarlılığı (Wavelet coherence) yöntemleri ile küresel ekonomik politika belirsizliğinin uluslararası ham petrol fiyatlarına yön verdiği görülmüş ve böylece enflasyon üzerindeki momentum gücünün önemli tarih aralıklarındaki duyarlılığı kanıtlanmıştır. Ayrıca para arzının bazı önemli tarih ve olaylar dinamiklerinde enflasyon üzerindeki yönlendirici etkileri de saptanmıştır. Bu bulgular, Türkiye bağlamında politika yapıcılar ve yatırımcılar için önemli çıkarımlar sağlamaktadır.

Modeling Oil shocks–Green Investments Nexus: A Global Evidence Based on Wavelet Coherence Technique

This study investigates the interdependence between oil shocks and green investments over time and frequency domains. Using the wavelet coherence approach, our results show evidence of bidirectional causality between all the variants of oil shocks and green investments around the global financial crisis and the 2014-2016 oil crisis. Economic activity shocks significantly Granger-cause green investments during the COVID-19 pandemic.