Wavelet Coherence Research Articles

Regional wood chip quality parameters decomposition and price linkage with impact on Polish energy sustainability: Time frequency analysis between 2013 and 2019

The study aims to analyze and decompose the qualitative parameters of wood chips in Poland. The European Green Deal brings the new framework to support sustainability and elimination of emissions. The Wavelet coherence and Wavelet Discrete Decomposition are used for determination of relations among significant qualitative parameters. Thus, the possible uses are discussed. For the obvious relationship between moisture and calorific value there is evidence of strong correlation. The behaviour of these interrelations are different at frequencies in the long and short time. The wood chip price is inter-transmitter from moisture parameter to calorific value in a positive (in-phase) relationship. At both low and high frequencies there is evidence that the variables of moisture and calorific value are highly correlated. The transient effect of linkage is presented at values between 0.1 and 0.3 in coherence map. The empirical findings provide implication for local producers and policymakers.

Do global uncertainties impede insurance activity? An empirical evidence from top two economies

Insurance companies have long-dated assets and liabilities, so their balance sheets are very sensitive to large changes in risk. Using global investor confidence, oil price uncertainty, world pandemic uncertainty and world uncertainty as proxies for global uncertainties, we examine the influence of global uncertainties on insurance activities in China and US to explain their interdependence and causality from 1992 to 2022. Running continuous wavelet transforms and wavelet coherence analysis, we found that while life and non-life insurance activity in Chinese and American market lags investor confidence and pandemic uncertainty, only non-life insurance activity in the US leads world uncertainty. Since the relation does not hold for their Chinese counterpart, this is not a global effect. Furthermore, the nexus between Chinese and American life insurance activity and oil price uncertainty is insignificant. We conclude that insurance activities in China and US adhere to demand-following hypothesis.

Analyzing time–frequency connectedness between cryptocurrencies, stock indices, and benchmark crude oils during the COVID-19 pandemic

We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmaz’s (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík’ (J Financ Econ 16:271–296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects.

Recession fears and stock markets: An application of directional wavelet coherence and a machine learning-based economic agent-determined Google fear index

Recession fears play a pivotal role in investment decision making and policy development aimed at reducing the likelihood of a recession and managing its impact. Using machine learning, we develop an economic agent-determined daily recession fear index using Google searches that isolates recession-related fears from overall stock market uncertainty. We study the evolving impact of recent recession fears on stock markets using directional wavelet coherence that distinguishes between positive and negative associations. Recession fears negatively impact world and G7 stock markets and trigger heightened volatility, with Japan being the most resilient. Monetary policy tightening in response to record inflation levels significantly contributes to persistent recession fears, suggesting that policymakers should consider co-ordinating responses to avoid an excessive global economic slowdown. Our methodology offers a high frequency monitoring tool that can be applied to analyse evolving relationships between variables and can be generalised to study the influence of specific events on financial markets by isolating topic-specific components from general proxies for uncertainty, attention or sentiment.

A multiscale analysis of returns and volatility spillovers in cryptocurrency markets: A post-COVID perspective

ABSTRACT Since the onset of the COVID-19 pandemic, leading cryptocurrencies have undergone significant price fluctuations, prompting widespread interest in the interdependence and spillover effects among cryptocurrency markets, as well as in identifying the key cryptocurrencies that drive market movements. This study contributes to the existing literature by utilising innovative vector wavelet coherence (VWC) and wavelet local multiple correlation (WLMC) frameworks to investigate the time-frequency co-movements among four cryptocurrencies (Bitcoin, Ethereum, Tether, and Binance). By exploring the co-movements across multiple time scales over a period from 01/01/2020 to 10/04/2023 through continuous and discrete wavelet coherency analysis, we identify four key empirical findings. Firstly, the returns connectedness is stronger than the volatility connectedness. Secondly, high-frequency co-movements are more erratic and correspond to positive and negative unexpected news, while low-frequency co-movements vary with changes in US monetary policy. Thirdly, Tether and Binance exhibit the weakest returns and volatility connectedness with other cryptocurrencies. Lastly, Ethereum and Tether (not Bitcoin) are the primary cryptocurrencies that account for returns and volatility movements in the market. We discuss the implications of these findings for various stakeholders in cryptocurrency markets.

Oil shocks and currency behavior: A dual approach to digital and traditional currencies

Given the financial markets' growing preference for examining digital platforms, this study explores the dynamic relationship of oil shocks with both conventional and digital currencies. It provides a novel approach for evaluating the relationship between aggregate oil, demand, and supply shocks using three conventional currencies (Japanese Yen, Chinese Yuan, and Euro), cryptocurrencies (Bitcoin, Ethereum, Tether), and DeFi tokens (Maker, Chainlink, and Basic Attention Token). We use wavelet analysis to test the asymmetric association among variables from June 22, 2018, to July 11, 2023. The outcomes of continuous wavelet confirm the volatile behavior of oil shocks and studied currencies. In particular, a nonlinear wavelet coherence is observed between oil shocks and DeFi tokens, cryptocurrencies, and traditional currency in the short, medium, and long term. Moreover, crypto and conventional currencies are found to respond more strongly to external events than DeFi tokens. Given the empirical findings and the rapid transformation of digitalized finance with ongoing currency restructuring, this study plays a critical role by actively influencing the adaptation of regulatory frameworks to align with dynamic changes in associations, serving as a strategic guide for regulatory bodies to navigate the complexities of emerging financial paradigms.

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.

Examining the environmental Phillips curve hypothesis in G7 nations: critical insights from wavelet coherence and wavelet causality analysis

Examining the environmental Phillips curve hypothesis in G7 nations: critical insights from wavelet coherence and wavelet causality analysis

Spatiotemporal changes in fine particulate matter and ozone in the oasis city of Korla, northeastern Tarim Basin of China

Air pollution is a serious environmental health concern for humans and other living organisms. This study analyzes the spatial and temporal characteristics of air pollutant concentrations, changes in the degree of pollution, and the wavelet coherence of the air quality index (AQI) with pollutants in various monitoring stations. The analysis is based on long-term time series data (January 2016 to December 2023) of air pollutants (PM2.5, PM10, and O3) from Korla, an oasis city in the northeastern part of the Tarim Basin, China. The concentrations of PM2.5, PM10, and O3 in Korla showed a cyclical trend from 2016 to 2023; PM10 concentrations exhibited all-season exceedance and PM2.5 exhibited exceedance only in spring. PM2.5 and PM10 showed a seasonal distribution of spring > winter > fall > summer; O3 concentrations showed a seasonal distribution of summer > spring > fall > winter. Strong positive wavelet coherence between PM and Air Quality Index (AQI) data series suggests that the AQI data series can effectively characterize fluctuating trends in PM concentrations. Moreover, PM10 levels IV and VI were maintained at approximately 10%, indicating that sand and dust have a substantial influence on air quality and pose potential threats to the health of urban inhabitants. Based on the results of this study, future efforts must strengthen relative countermeasures for sand prevention and control, select urban greening species with anti-pollution capabilities, rationally expand urban green spaces, and restrict regulations for reducing particulate matter emissions within city areas.

Application of the Data-Driven Method and Hydrochemistry Analysis to Predict Groundwater Level Change Induced by the Mining Activities: A Case Study of Yili Coalfield in Xinjiang, Norwest China

As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R2) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities.

Dynamic interactions between oil prices and renewable energy production in Italy amid the COVID-19 pandemic: wavelet and machine learning analyses

This study examines the intricate dynamics between oil prices and renewable energy investments in Italy during the initial phase of the CoronaVirus Disease 2019 pandemic, a period characterized by significant economic and social upheaval. Utilizing advanced empirical techniques, such as Partial Wavelet Coherency analysis, Time-Varying Granger Causality, and Robinson Log-Periodogram tests, as well as Machine Learning (ML) regressions, this research uncovers nuanced insights into the interplay between oil prices and renewable energy series including biomass, solar, hydro, wind, and geothermal. Key findings indicate a predominant in-phase relationship with oil prices leading most renewable energy series, and unidirectional causality from renewables to oil prices in several instances, highlighting the potential influence of renewable energy on oil market dynamics. In robustness checks, ML models further elucidate the impact, with solar, hydro, and geothermal sources showing significant importance scores. These insights are critical for policymakers and stakeholders aiming to enhance energy security and transition towards sustainable energy sources amidst global crises.

Temporal Evolution, Oscillation and Coherence Characteristics Analysis of Global Solar Radiation Distribution in Major Cities in China’s Solar-Energy-Available Region Based on Continuous Wavelet Transform

The majority of the energy required for human survival is derived either directly or indirectly from solar radiation, thus it is important to investigate the periodic fluctuations in global solar radiation over time. In this study, six cities—Harbin, Shenyang, Beijing, Shanghai, Wuhan, and Guangzhou—located in the utilizable areas of solar energy in China, were selected, and the periodicity of the daily global solar radiation of these six cities with time were investigated by means of wavelet power spectrum analysis. Furthermore, Harbin, which has the lowest monthly average of global solar radiation in the cold of winter, was selected to explore the temporal evolution relationship between global solar radiation and other meteorological factors, and air quality factors by wavelet coherence analysis. Among the meteorological factors, the correlation between global solar radiation and sunshine duration exhibits the highest level of consistency. Global solar radiation demonstrates a positive association with atmospheric temperature/wind speed/precipitation over the annual cycle. Conversely, it displays a negative correlation with atmospheric pressure during this time frame. Additionally, on shorter timescales, global solar radiation is negatively correlated with air humidity and precipitation. Among air quality factors, the relationship between global solar radiation and CO/NO2/O3/PM2.5/PM10/SO2 primarily manifests in the broader annual cycle time scale. Within this context, global solar radiation shows a positive correlation with O3, while displaying negative associations with the other five air quality indicators. These findings contribute to urban environmental planning and the effective management and utilization of solar radiation, thereby providing valuable insights to guide the future development of cross-regional comprehensive energy utilization projects under diverse climatic and geographical conditions. Additionally, they serve as a reference for subsequent research aimed at enhancing the accuracy of global solar radiation prediction models.

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.

Spatial diversity of atmospheric moisture transport and climate teleconnections over Indian subcontinent at different timescales

Regional weather and climate are generally impacted by global climatic phenomenon′s. Understanding the impact of global climate phenomenon′s on an atmospheric branch of the hydrological cycle is crucial to make advances in skillful precipitation forecast. The present study adopts a multiscale approach based on wavelets for unravelling the linkages between teleconnections and atmospheric moisture transport over homogeneous regions of Indian sub-continent. We investigated linkages between atmospheric moisture transport quantified as monthly integrated water vapor transport (IVT) during 1951–2022 over selected homogeneous regions and eight large scale climate oscillations using wavelet and global wavelet coherence. Our results indicate significant heterogeneity in linkages across different regions and across multiple timescales. In particular, the Indian Ocean Dipole (IOD) influence monthly IVT at intra-annual to inter-annual scale over all regions. The El Niño–Southern Oscillation (ENSO) have strong connection to monthly IVT at inter-annual scale whereas over west central region both IOD and ENSO strongly influence IVT at inter-decadal scale. While the Atlantic Multi-Decadal Oscillation and Pacific Decadal Oscillation have an impact on IVT in the north-east and southern regions, the Arctic Oscillation and North Atlantic oscillation have a strong inter-annual connection to IVT, majorly in the northwest and hilly regions. Overall, the methodology offers an effective approach for capturing the dynamics of atmospheric moisture transport in time–frequency space and provide a practical reference for prediction of atmospheric moisture transport linked precipitation over different regions of Indian subcontinent.

The Growth Effect of Foreign Direct Investment: Evidence from Time-Frequency Analysis

The impact of FDI inflow on the economy of the host country had not been clearly defined, especially in the empirical literature. Most of the empirical results from past studies are distorted by model misspecification and endogeneity problems. To find a cure for these deficiencies, this study uses an unconventional methodology called wavelet coherence to examine the nexus between FDI inflows and per capita income growth in three African countries (The Gambia, Ghana, and Senegal. Wavelet coherence is a localized correlation in time-frequency space that examines the dynamic nexus between two time series. Most traditional econometric methods that were used to study the nexus between FDI inflows and income assumed that the series are stationary, however, most economic time series are not stationary, hence, this makes most traditional methods ineffective in finding the nexus between FDI and income. The use of wavelet coherence can overcome this challenge because it does not require the assumption of stationarity of the data.The empirical results of the study showed that the impact of FDI inflows depends on the degree to which the FDI is a complement or a substitute for domestic investment. Senegal where FDI complements domestic investment in enhancing growth derived more benefit from FDI inflows than The Gambia where FDI inflows substitute domestic investment and Ghana where FDI inflows had no impact on domestic investment. The causality between per capita income growth and per stock of FDI inflows runs from per stock of FDI inflows or the two series move together.

Coherency and time lag analyses between MODIS vegetation indices and climate across forests and grasslands in the European temperate zone

Abstract. Identifying the climate-induced variability in the condition of vegetation is particularly important in the context of recent climate change and plants' impact on the mitigation of climate change. In this paper, we present the coherence and time lags in the spectral response of three individual vegetation types in the European temperate zone to the influencing meteorological factors in the period 2002–2022. Vegetation condition in broadleaved forest, coniferous forest and pastures was measured with monthly anomalies of two spectral indices – normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI). As meteorological elements we used monthly anomalies of temperature (T), precipitation (P), vapour pressure deficit (VPD), evapotranspiration (ETo), and the teleconnection indices North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP). Periodicity in the time series was assessed using the wavelet transform, but no significant intra- or interannual cycles were detected in both vegetation (NDVI and EVI) and meteorological variables. In turn, coherence between NDVI and EVI and meteorological elements was described using the methods of wavelet coherence and Pearson's linear correlation with time lag. In the European temperate zone analysed in this study, NAO produces strong coherence mostly for forests in a circa 1-year band and a weaker coherence in a circa 3-year band. For pastures these interannual patterns are hardly recognisable. The strongest relationships occur between conditions of the vegetation and T and ETo – they show high coherence in both forests and pastures. There is a significant cohesion with the 8–16-month (ca. 1-year) and 20–32-month (ca. 2-year) bands. More time-lagged significant correlations between vegetation indices and T occur for forests than for pastures, suggesting a significant lag in the forests' response to the changes in T.

Indian energy policies amidst urban population and economic growth: Sourcing evidence from time-frequency domain analysis

Relying solely on fossil fuel-driven economic growth could prove unsustainable, particularly in India, where the adverse environmental impact of this growth trajectory is increasingly affecting overall economic development. This challenge may hinder the realization of the sustainable development goals (SDGs) in the country. Therefore, to address this policy concern, an empirically investigation on the influence of various growth drivers on India's economic growth from 1965 to 2019 was conducted by this present study. Leveraging time-frequency estimators like gradual shift causality tests and Wavelet Coherence, the study yields a range of findings that can inform the formulation of an SDG-oriented policy framework. These outcomes not only acknowledge the detrimental environmental consequences of economic growth but also provide insights into addressing energy security issues. Ultimately, this research enhances to the literature by suggesting a comprehensive and adaptable policy framework aimed at advancing SDGs 7, 8, 12, and 13.

Variability in receivers responses of MASW test on undulated grounds: A numerical perspective

This article presents methodological and signal processing cautions associated with utilizing the multichannel analysis of surface waves (MASW) test for determining shear wave velocity (Vs) profiles on undulated ground surfaces. The past studies highlight that the conventional MASW test, which is suitable for flat ground surfaces, may not directly apply to undulated grounds with features like heave, crest, etc. Previous studies on MASW test lack an understanding of the influence of source and receiver positioning on various undulations. Three scenarios of ground undulations were created in GiD software and analyzed in the OpenSEES software framework. There were 12 receivers used for these ground undulations based on the Fourier amplitude spectrum attenuation. The receivers' responses (horizontal and vertical components of wavefields) from these ground undulations were analyzed through signal processing using cross-correlation, time-lag, and wavelet coherence. Notably, the undulated topography significantly impacted the vertical components of the wavefield. The phase angle variations from wavelet coherence could be used to determine the minimum frequency of the dispersion curve for these ground undulations. Significant variations in the phase velocity were observed at lower frequencies in the presence of ground undulations, while on a flat surface, they remained unaffected. The effect of under−/over-estimation of Vs (in the range of 50–100 m/s) was successfully demonstrated in each respective undulation with the methodological and processing cautions needed in the MASW analysis.

A multiresolution analysis of NeoFisher effects in industrialized economies: Have monetary policy dynamics being misconstrued in the west?

Central Banks in major industrialized economies have struggled to keep inflation within their target ranges since the global financial crisis. The periods of missing inflation and deflation experienced in the post-crisis era have led to doubts about the traditional Fisher effect as a crucial component of the policy reaction function. We explore the NeoFisherian theory, which suggests an alternative causal relationship between the policy rate and inflation. We focus on the USA, UK, and EU and apply wavelet coherence analysis to examine the time–frequency lead–lag comovements on data spanning from January 2007 to March 2023. Our findings indicate increasing NeoFisherian dynamics, notably post-2013 taper tantrum, implying that Central Banks in industrialized economies have misunderstood monetary policy dynamics.

The Bitcoin‐agricultural commodities nexus: Fresh insight from COVID‐19 and 2022 Russia–Ukraine war

AbstractThis paper investigates the volatility connectedness and dynamic time–frequency relationship between Bitcoin (BTC) and 15 major agricultural commodity markets during the COVID‐19 and 2022 Russia–Ukraine war periods. We employ the TVP‐VAR‐based extended joint connectedness method, minimum connectedness investment portfolio, and wavelet coherence (WC) method. The results indicate that the sudden outbreaks of the two crises brought about increased volatility connectedness between BTC and agricultural commodity markets. Throughout the entire sample period, BTC remained a net transmitter of volatility. Moreover, in terms of the total connectedness index (TCI), the overall volatility correlation surged rapidly after the outbreak of COVID‐19 and the 2022 Russia–Ukraine war. The portfolio results demonstrated that BTC exhibited a low correlation with the agricultural commodity markets, suggesting diversification potential. Additionally, only Feeder Cattle served as an effective hedging asset for BTC throughout all periods. The WC analysis confirmed that during the COVID‐19 period and the 2022 Russia–Ukraine war, most of the linkages were primarily concentrated at medium‐ to long‐term frequencies. Our analysis will contribute to a deeper understanding of the interconnection between these markets, enabling market participants to consider risk mitigation measures and support portfolio diversification when formulating policies and regulations involving relevant markets in the future.