Wavelet Coherence Research Articles

Cyclinaclity Effects of Exchange Rates and Oil Prices

Understanding the concept of time scales is crucial when modeling economic and financial decisions. Within the time-frequency domain, this study delves into the relationship between fluctuations in oil prices and exchange rates across major oil-importing and exporting countries. The investigation employs various cross-wavelet techniques within the continuous wavelet transform framework, with a particular focus on wavelet coherence and phase-difference over the period 2000 to 2020. The results underscore a notable diversity in the connection between the oscillations of oil prices and exchange rates across diverse countries. This relationship is subject to temporal variations and is contingent upon the specific time horizon under consideration. In particular, our analysis reveals strong co-movements between oil prices and exchange rates across various time intervals and frequencies. Importing oil countries like New Zealand, Singapore, Brazil, and Taiwan exhibit particularly pronounced co-movements. Similarly, exporting oil countries such as Kuwait, Mexico, Russia, and Canada also display strong associations between oil prices and exchange rates. These correlations are intricately tied to key macroeconomic events, further highlighting the complex interplay between oil prices and exchange rate movements in different global regions. While a robust connection is evident in numerous countries, the strength of the relationship appears significantly weaker in several others. This variance underscores the nuanced nature of the association between the fluctuations in oil prices and exchange rates across the global landscape.

SST trends in certain areas of the Barents Sea in the winter season and mechanisms of their formation

The climate changes observed over the past few decades are most clearly manifested in the Arctic Ocean. Sea surface temperature (SST) is one of the most reliable indicators of climate change. In this paper we analyze the changes of winter SST for the western, northeastern and southeastern regions of the Barents Sea and examine the relationship of the emerging STS trends with the influence of various external factors. The working data set is represented by average monthly SST values taken from the ERA-5 reanalysis for the period 1949–2023 with a spatial resolution of 0.25×0.25° and average water temperature values on the Kola Meridian section in the 0–50 m layer. Additionally, the Arctic Oscillation (AO), Arctic Dipole (AD) and Atlantic Multidecadal Oscillation (AMO) indices were used as external factors that may affect SST variability. The time series analyzed was divided into three periods: 1949–1969, 1970–1990, 1991–2023, where the variability of the analyzed parameters was different. Thus, in the first period the trend in SST changes was negative, for the second period it was slightly negative or neutral, and for the third period it was positive. It is shown that SST in all the regions of the Barents Sea has undergone significant changes, which were most noticeable in the “warm” period of 1991–2023, when the rate of SST increasing was up to 10·10-2 °C/year in areas under the warm Atlantic water influence. The analysis of SST variability in the Barents Sea shows that the positive anomalies observed in the recent years are most likely associated with the changes in the atmospheric circulation. The Wavelet coherence analysis showed the closest agreement between the changes in the sea surface temperature and the AD index in the winter season, and with the AMO index.

The spillover effects among financial stress, investor sentiment, and GCC stock markets: evidence under the bearish and bullish market states

ABSTRACT This study delves into an exploration of volatility spillover between financial stress, investor sentiment, and stock market index returns in the Gulf Cooperation Council (GCC) countries using the DCC-GARCH, wavelet coherence, and BEKK-GARCH models. A robustness check, applying Diebold and Yilmaz’s (2012, 2014) methodology, verifies network measures across markets. Significant patterns in financial stress index were observed during crises, with strong co-movement between financial stress, investor sentiment, and stock returns, especially during COVID-19 pandemic. Volatility transmission from stock returns to investor sentiment and the financial stress index was notable in some markets. Additionally, we found a significant volatility transmission from the financial stress index to market returns during periods of bearish stress in Bahrain and Kuwait and bullish stress across all countries. These findings offer insights for investors and fund managers optimizing portfolios and managing risk exposure.

The spillover and comovement of downside and upside tail risks among crude oil futures markets

Combining efficient transfer entropy and DY spillover indices, this study first explores the contagion effects of downside and upside tail risks among INE, Brent, and WTI. Then we utilize wavelet coherence to capture the comovement characteristics of tail risks at multi-time scales. Furthermore, by mapping wavelet coherence information into comovement networks, the diversity and regularity of mode conversions of tail interdependency are investigated in depth. The results show that INE is reactively susceptible to the tail risk spillovers from Brent and WTI, and in terms of the magnitude of spillover to INE, Brent surpasses WTI prominently. The coherence between the tail risks of INE and Brent or WTI is much weaker than that of the Brent-WTI pair, especially in the short term or in the upside tail case. The evolution of downside tail risk comovement is more difficult to stabilize. A few comovement modes show a significantly higher probability of occurrence than others, and they also tend to link each other. Of special note are some comovement modes with low probability of occurrence yet behave as the key “bridges” in the mode transformation process.

Understanding the interplay of GDP, renewable, and non-renewable energy on carbon emissions: Global wavelet coherence and Granger causality analysis

This study examines the causality of Per Capita Gross Domestic Production (PGDP), Renewable Energy Consumption (REC), and Non-Renewable Energy Consumption (NREC) on Carbon dioxide (CO2) emissions at the global level utilising data gathered from 1995 to 2020 across various countries categorised based on income levels as High, Low, Upper Middle and Lower Middle and analysed through wavelet coherence. The findings reveal both bidirectional and unidirectional causality between the variables which have evolved. Globally, a bi-directional relationship is observed with a positive correlation between PGDP and NREC and in contrast, a negative correlation with REC. Furthermore, the analysis highlights varying causalities between CO2 emissions and PGDP, except for high-income and lower-middle-income country categories, all other shows one-way causality in different periods in the short term. Moreover, CO2 and REC, show unidirectional causality throughout the short-term, exceptionally medium & long term have both unidirectional and bidirectional causalities across all country categories with a positive correlation. In contrast, CO2 and NREC depict similar causalities to REC, however, with a negative correlation. A cross-country analysis was performed between CO2 and PGDP, CO2 and REC, and CO2 and NREC using Granger causality which shows mixed relationships. The findings hold significant implications for policymakers, providing valuable insights into the trade-offs between economic growth, energy consumption, and carbon emissions.

Safe Havens Characteristics of Islamic Stock Indices During Bearish and Bullish Markets: A Case of Developed and Developing Countries

Purpose — This study examines the performance of Islamic and conventional indices during bullish (positive) and bearish (negative) markets while the economic and financial conditions are different. Design/Methodology/Approach — This study uses GARCH (1,1) and wavelet-assisted cross-spectral, cross-correlation analyses to investigate the performance of Islamic and conventional stock indices and study the presence of safe havens, which reflect low volatility in the indices during times of market turbulence, for investors during bearish and bullish periods from 2019–2021. Findings — The paper finds that there is high and persistent volatility in Islamic indices in bullish markets, while in bearish periods the Islamic indices show low volatility persistence; and conventional indices were highly volatile during crisis periods. Also, the impact of news shocks on Islamic indices was intense before COVID-19, while after that news, shocks had a high impact on the conventional indices. The wavelet coherence analysis shows that there is high coherency between Islamic and conventional indices in developed countries but in developing countries, the opposite prevails. Originality/Value — The study is the first to use both Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and wavelet coherence models for examining Islamic and conventional stock indices during bearish and bullish markets, especially in the COVID-19 pandemic era. Practical Implications — The results conclude that there are safe havens present in Islamic indices in both developing and developed countries, especially the United States (US), the United Kingdom (UK), India, and Indonesia, while in other countries the presence is weak. Both GARCH and wavelet coherence models show the same results regarding the presence of volatility and safe havens for investors in different countries.

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.

Global uncertainties and stock returns under heterogeneous market conditions: Evidence from wavelet coherence analysis

Over the years, the world has faced numerous global uncertainties, each leaving a profound impact on the global economy and the financial market. This has increased the research on how these uncertainties affect various stocks. However, limited attention has been directed toward comprehending the combined influence of geopolitical risk, global policy uncertainty, and infectious disease equity market volatility on heterogeneous stock returns. This study sets out to investigate the impact of geopolitical risk (GPRH), global policy uncertainty (GPU), infectious disease equity market volatility (ID-EMV), and their correlations on stock returns in a heterogeneous market context, employing wavelet coherence analysis. The study's outcomes emphasize the temporal nature of these uncertainties’ effects on financial markets. Furthermore, our research shows that there is a stronger influence on stock returns from various markets when the combined effects of GPU, ID-EMV, and GPRH are taken into account than when the effects are taken into account separately. This implies that times when all of these factors are operating concurrently will see greater fluctuations in markets than times when only one of them acts in the market. The findings of the study provide significant insight into the complex relationship between economic indicators and stock market performance, allowing investors and policymakers to gain a better understanding of the dynamics of global financial markets.

Food security in Nigeria amidst globalization, economic expansion, and population growth: A wavelet coherence and QARDL analysis

Food security is crucial on a global scale as it provides essential health, social, and economic advantages to the human population. Therefore, to achieve the sustainable development goal of zero hunger (Goal 2), it is imperative to assess the progress of food security, particularly in developing countries. Thus, this reflects on the relative factors affecting food security in Nigeria from 1980 to 2022. The study utilized advanced econometrics approaches such as Quantile Autoregressive Distributed Lag (QARDL) and Wavelet Coherence (WTC) to analyze the data. Food security is measured using the food production index (FPI), while other variables such as globalization (GLOB), population growth (POP), gross domestic product (GDP) as a measure of economic growth, and inflation are considered. The results of the Quantile Autoregressive Distributed Lag analysis indicate that in the short-term, globalization has a beneficial impact on FPI at the 0.25 quantile, but it has a detrimental effect on food security at the 0.50 and 0.75 quantiles. Nevertheless, over a prolonged period, the effect is detrimental in all quantiles. Additionally, in the short-term, FPI experiences a beneficial effect from GDP in all quantiles. Over time, GDP has a positive effect on all levels of income distribution. However, in the short-term, the size of the POP has a negative effect on FPI for the 0.25 to 0.75 quantiles. Further discussion is dedicated to policy options and their implications.

Mediating role of energy uncertainty for environmental management in electricity generation: The evidence from Pakistan

This groundbreaking study examines the relationship between CO2 intensity, aggregated and disaggregated fossil fuels, clean and nuclear energy, and, as a mediating variable, energy uncertainty for Pakistan during 2019M01 and 2022M10 with monthly data. To this end, the ARDL Bound Testing method is used to identify the long-run relationship of the studied factors. The empirical results suggest that under the mediating effect of energy uncertainty, renewables and nuclear energy in electricity generation have a negative association with CO2 intensity. In contrast, fossil fuels in generating electricity influence positively CO2 intensity in the aggregated analysis. Moreover, the disaggregated results under the mediating role of energy uncertainty reveal that only hydro energy reduces CO2 intensity as renewables, bioenergy, wind, and solar energy do not impact CO2 intensity. Both coal and gas energies cause a rise in CO2 intensity. Regarding nuclear energy, it also has a negative relation with CO2 intensity. The increase in energy uncertainty leads to a fall in CO2 intensity in aggregated and disaggregated analyses as well. Wavelet coherence analysis shows that CO2 intensity and energy uncertainty depend on each other dynamically for almost the whole employed period.

The nexus between clean energy market risk and US business environment: evidence from wavelet coherence and variance analysis

The nexus between clean energy market risk and US business environment: evidence from wavelet coherence and variance analysis

Analysis of Wavelet Coherence in Calf Agonist-Antagonist Muscles during Dynamic Fatigue.

Dynamic muscle fatigue during repetitive movements can lead to changes in communication between the central nervous system and peripheral muscles. This study investigated these changes by examining electromyogram (EMG) characteristics from agonist and antagonist muscles during a fatiguing task. Twenty-two healthy male university students (age: 22.92 ± 2.19 years) performed heel raises until fatigue. EMG signals from lateral gastrocnemius (GL) and tibialis anterior (TA) muscles were processed using synchrosqueezed wavelet transform (SST). Root mean square (RMS), mean frequency (MF), power across frequency ranges, wavelet coherence, and co-activation ratio were computed. During the initial 80% of the task, RMS and EMG power increased for both muscles, while MF declined. In the final 20%, GL parameters stabilized, but TA showed significant decreases. Beta and gamma intermuscular coherence increased upon reaching 60% of the task. Alpha coherence and co-activation ratio remained constant. Results suggest that the central nervous system adopts a differentiated control strategy for agonist and antagonist muscles during fatigue progression. Initially, a coordinated "common drive" mechanism enhances both muscle groups' activity. Later, despite continued increases in muscle activity, neural-muscular coupling remains stable. This asynchronous, differentiated control mechanism enhances our understanding of neuromuscular adaptations during fatigue, potentially contributing to the development of more targeted fatigue assessment and management strategies.

COVID-19 and Uncertainty Effects on Tunisian Stock Market Volatility: Insights from GJR-GARCH, Wavelet Coherence, and ARDL

This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended.

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.

Spatial and Temporal Variations’ Characteristics of Extreme Precipitation and Temperature in Jialing River Basin—Implications of Atmospheric Large-Scale Circulation Patterns

In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based on the daily precipitation and atmospheric temperature datasets from 29 meteorological stations in JRB and its vicinity from 1960 to 2020, 10 extreme indices (6 extreme precipitation indices and 4 extreme temperature indices) were calculated. The spatial and temporal variations of extreme precipitation and atmospheric temperature were analyzed using Mann–Kendall analysis, to explore the correlation between the atmospheric circulation patterns and extreme indices from linear and nonlinear perspectives via Pearson correlation analysis and wavelet coherence analysis (WTC), respectively. Results revealed that among the six selected extreme precipitation indices, the Continuous Dry Days (CDD) and Continuous Wetness Days (CWD) showed a decreasing trend, and the extreme precipitation tended to be shorter in calendar time, while the other four extreme precipitation indices showed an increasing trend, and the intensity of precipitation and rainfall in the JRB were frequent. As for the four extreme temperature indices, except for TN10p, which showed a significant decreasing trend, the other three indices showed a significant increasing trend, and the number of low-temperature days in JRB decreased significantly, the duration of high temperature increased, and the basin was warming continuously. Spatially, the spatial variation of extreme precipitation indices is more obvious, with decreasing stations mostly located in the western and northern regions, and increasing stations mostly located in the southern and northeastern regions, which makes the precipitation more regionalized. Linearly, most of the stations in the extreme atmospheric temperature index, except TN10p, show an increasing trend and the significance is more obvious. Except for the Southern Oscillation Index (SOI), other atmospheric circulation patterns have linear correlations with the extreme indices, and the Arctic Oscillation (AO) has the strongest significance with the CDD. Nonlinearly, NINO3.4, Pacific Decadal Oscillation (PDO), and SOI are not the main circulation patterns dominating the changes of TN90p, and average daily precipitation intensity (SDII), maximum daily precipitation amount (RX1day), and maximum precipitation in 5 days (Rx5day) were most clearly associated with atmospheric circulation patterns. This also confirms that atmospheric circulation patterns and climate tend not to have a single linear relationship, but are governed by more complex response mechanisms. This study aims to help the relevant decision-making authorities to cope with the more frequent extreme climate events in JRB, and also provides a reference for predicting flood, drought and waterlogging risks.

Examining relations between sea-level anomalies in the Niño 3.4 region and terrestrial hydroclimatic conditions in China

As a key driver of global climate variability, El Niño–Southern Oscillation (ENSO) significantly impacts the regional hydrological cycle. Similar to the recognized ENSO monitoring index (i.e., sea surface temperature anomaly), a potential advancement in the usage of sea level anomaly (SLA) in the equatorial Pacific is expected to enhance our knowledge of ENSO and its influence on regional terrestrial hydroclimatic conditions (THCs). In this study, we employed wavelet coherence and multi-channel singular spectral analysis to investigate the ability of satellite altimetry-based SLA, and its two major components (i.e., steric SLA (SSL) and non-steric SLA (NSSL)) averaged over Niño 3.4 region, to identify ENSO-related signals. Subsequently, we explored their spatial association with THCs at interannual scales in China using a cross-correlation method. We found that the time series of sea surface temperature anomaly index (SSTaI), SLA index (SLAI), and SSL index (SSLI) displayed high consistency. SSTaI was found to present higher correlations to SLAI (0.91) and SSLI (0.88), compared to NSSL index (NSSLI; 0.40). Furthermore, the high (low) wavelet coherence and in-phase (orthogonal) relationship between SSTaI and SLAI/SSLI (NSSLI) was also revealed. This indicated that SLAI and SSLI may be good complementary indices to SSTaI in observing ENSO-related signals. Similar to the temporal association of SSTaI and SLAI/SSLI, their spatial correlation pattern with THCs also displayed consistency. The time lag characterizing the relationship between SSTaI/SLAI/SSLI and THCs was also consistent, despite some regional differences. In summary, our results indicated that the combined analyses of the time lag determined between SSTaI/SLAI/SSLI and THCs can eventually complement the time lag analysis based solely on SSTaI. This, in turn, provides a more comprehensive understanding of ENSO’s influence on THCs in China.

Spatiotemporal variation and teleconnections of extreme precipitation in the Upper Indus Basin: insights for natural hazard assessment

ABSTRACT The study investigates trends and teleconnections of extreme precipitation events in the Upper Indus Basin (UIB) within the Indian region, part of the crucial geo-ecological Hindu Kush Himalayan Mountain system. Utilizing high-resolution (10km) HAR data from 2002 to 2013, we analyzed 11 indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) to observe variations in extreme precipitation at the monthly, seasonal, and annual scales. Results show an increase in dry and wet extreme precipitation frequency and intensity, increased monsoon precipitation, and a shift of winter precipitation, increasing continuous dry days. Using APHRODITE daily (0.25°) data from 1952 to 2014, continuous wavelet transform and wavelet coherence analysis indicate significant periodicities and correlations with large-scale climate anomalies such as El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Wavelet coherence analysis reveals that monthly extreme precipitation events significantly correlate with ENSO at 3-5 years periodicity, while annual extremes show significant coherence with NAO at 8-10 years and over 16 years periodicities. The study highlights the impact of global climate change on regional precipitation and the need for adaptive water management policies to mitigate flood risks during the rainy season and address water scarcity in the dry season.

Carbon exchange-traded funds market and economic policy uncertainty

This study empirically analyses the relationship between economic policy uncertainty (EPU) and three carbon exchange-traded funds (ETFs) using wavelet coherence analysis as well as dynamic conditional correlation (DCC) from a multivariate GARCH model. The analysis techniques employed allow us to analyze the nature of the relationship which may depend on time and frequency. Our findings indicate that there exists a significant negative correlation between EPU and the ETFs for almost all times and frequencies. However, for some times and frequencies, the identified negative relationship is not significant. Interestingly, we also find that for some times and frequencies, EPU leads the ETFs and for some other times and frequencies ETFs lead the EPU.

Connectedness and frequency connection among green bond, cryptocurrency and green energy-related metals around the COVID-19 outbreak

We investigate the return interdependence among green bonds, cryptocurrency indices and green energy-related metals. We apply time-varying parametric vector autoregression (TVP-VAR) conenctedness, wavelet coherence, Wavelet Quantile Correlation （WQC) and Quantile on Quantile (QQR) Connectedness Methods. Our empirical findings show that return connectedness has become even stronger after the outbreak of COVID-19, with both green bonds and cryptocurrency indices acting as net receivers of return spillovers. Surprisingly, Copper functioned as a net sender of return spillovers over the entire observation period. Findings revealed that the cryptocurrency index exhibited a consistent positive correlation with the green energy-related metals market at medium to short-term frequencies, whereas green bonds showed a negative correlation with metals market at short-term frequencies and a positive correlation at long-term frequencies.

Prediction of streamflow in Chalakudy River Basin, Kerala, by integrating teleconnection patterns of large-scale atmospheric circulations

ABSTRACT In this study, the relationship between large-scale climatic drivers and streamflow of the Chalakudy River Basin, Kerala, was analysed using methods such as bivariate wavelet coherence (BWC), multiple wavelet coherence (MWC), and partial wavelet coherence (PWC) analysis. The four prominent global climate indices chosen are the Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO), along with other local climate drivers. The BWC analysis showed that streamflow and rainfall had a very strong in-phase relationship, whereas the maximum temperature and average temperature showed an anti-phase relationship. In the case of global climate drivers, ENSO has a significant impact on the streamflow of the Chalakudy River Basin. The average wavelet coherence (AWC), which quantifies the teleconnections, confirms the observation with a high coherency of 0.75 between streamflow and rainfall and 0.51 for streamflow and ENSO. Streamflow prediction models were developed using random forest (RF) and artificial neural network (ANN) techniques by considering the influence of significant global and local climatic drivers. It was observed that the RF model performed slightly better than the ANN model, with R = 0.875, NSE = 0.766, RMSE = 23.468, and RSR = 0.524.