Wavelet Coherence Analysis Research Articles

Virtual land in the metaverse? Exploring the dynamic correlation with physical real estate

PurposeAs blockchain-based virtual worlds gain prominence within the emerging metaverse and Web3, numerous global companies and investors are buying purely virtual land to explore new business potentials and capitalize on digital assets. Given the similarities to physical real estate, this study examines the dynamics of the secondary market for virtual land and relates its returns to those of physical real estate.Design/methodology/approachUsing transaction-level data from a prominent virtual land platform, the authors construct a virtual land market index based on repeat sales index methodology from traditional real estate studies. Wavelet coherence analysis is employed to examine the dynamic correlation between virtual land and various physical real estate market returns. The determinants of this correlation are estimated using stepwise regression analysis. A portfolio analysis explores the implications of adding virtual land to traditional asset portfolios.FindingsThe correlation between virtual and physical real estate market returns is generally low, reaching its lowest during the Covid-19 lockdowns from 2020 to 2022. It spikes during acute economic turmoil such as the initial Covid-19 outbreak or interest rate change announcements. The correlation is primarily driven by consumer and economic climate, the price of the virtual economy token and investor attention. Portfolio analysis indicates that virtual land can enhance risk-adjusted returns within a traditional portfolio, particularly when added to a commercial real estate portfolio.Research limitations/implicationsThis study examines a single virtual land market, despite it being the oldest and one of the largest. Given the rapidly evolving nature of virtual worlds, it is crucial to further test the results and include new virtual land platforms as they emerge.Practical implicationsThe findings provide actionable insights on portfolio implications for investors seeking alternative real-estate-like assets in the digital space. Additionally, this study offers strategic guidance for entering the metaverse, including a comprehensive overview of established virtual presences.Originality/valueWith the advancing digitization of real estate markets, this study is the first to explore the correlation between market returns of virtual land in the metaverse and traditional physical real estate. The findings provide valuable empirical insights for investors, policymakers, entrepreneurs and companies interested in the intersection of digital and traditional property markets.

A synergetic approach for multidimensional drought assessment in the Indian agro-climatic zone using coherency, propagationand AHP techniques.

Drought is one of the most detrimental natural calamities to the economy. Despite its significant consequences, the evolution from meteorological to agricultural and hydrological droughts still needs to be explored. A thorough investigation was carried out in India's eastern hills and plateau region to determine the extent of drought's impact through indices. Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSMI), and Standardized Streamflow Index (SSI) were analyzed to delineate meteorological, agricultural, and hydrological droughts across the region using precipitation, temperature, soil moisture, and streamflow data comprising of the 35years (1987-2021) from 24 stations. Analytic Hierarchy Process (AHP) was employed with Geographic Information System (GIS) techniques for combined drought indicator. The findings show that compared to other indices, the maximum weightage is given to meteorological drought indices (SPI and SPEI). The combined drought index (CDI) resulted from 39.1% of SPI, 27.6% of SPEI, 19.5% of SSMI, and 13.8% of SSI. The AHP technique made it easier to generate maps. These maps categorize the area into five degrees of vulnerability, from severe drought to normal conditions. The propagation duration from the onset of meteorological drought to the emergence of agricultural and ultimately hydrological drought was explored. The propagation time of hydrological drought after meteorological drought is longer compared to agricultural drought. Additionally, wavelet coherence analysis investigated the interaction of meteorological drought with agricultural and hydrological drought in the region. Temporal assessments of the spatial extent of drought across the study area were also conducted, offering crucial details about the drought's regional and temporal pattern. This multidimensional method provides a thorough learningof the dynamics and sensitivity of drought in the eastern hills of India and the plateau region. This is helpful for the implementation of targeted mitigation and adaptation strategies to reduce the adverse impacts of drought.

Navigating global economic turmoil: The dynamics of oil prices, exchange rates, and stock markets in BRICS

The study aims to analyze the co-movement between oil prices, BRICS nations’ exchange rates, and stock markets. Grasping these interrelationships is essential for understanding how global energy price shifts broadly affect the economies, particularly those of developing nations.The study employs wavelet coherency analysis on daily data, examining the association between crude oil (Brent crude), exchange rates (Brazilian Real, Russian Rubble, Indian Rupee, Chinese Yuan, and South African Rand), and stock markets (BOVESPA of Brazil, Moscow Exchange of Russia, Nifty50 of India, Shanghai Composite of China, and JSE FTSE of South Africa) across both temporal and frequency domains.This study reveals strong comovements, especially during periods of global economic instability, such as the impact of the COVID-19 pandemic and the Russia-Ukraine war. During such periods, oil prices and stock market indices tend to move in tandem, while oil prices and exchange rates show an inverse relationship. The study also reveals a decoupling of crude oil from both share markets and exchange rates during normal economic conditions. This decoupling suggests that outside of a chaotic period, the relationships weaken. However, the co-movements among the variables for China are notably weaker, even during economic upheavals, than in other BRICS nations. Understanding these relationships can aid in informed decision making and strategies in the face of global economic turmoil. AcknowledgmentThis study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2025/R/1446).

Impact of the Volume of Developer Housing Units on Real Estate Prices in Poland: Correlation and Coherence Analysis

This article conducts a comprehensive correlation analysis to explore the relationship between the development volume by developers and average market prices in Poland's housing sector from 2010 to 2023, utilizing quarterly data on the number of apartments released and their average prices. It employs both linear and nonlinear correlation analysis alongside wavelet coherence analysis. Preliminary correlational analysis offered insights into the basic interdependency patterns, highlighting how developer-supplied apartment numbers impact average prices. Wavelet coherence analysis, a more sophisticated approach, decomposed the data across various frequencies to uncover complex, nonlinear relationship patterns potentially missed by conventional correlation methods. The findings of the study highlight significant connections between the variables that vary over time and space, emphasizing the complexity of the housing market and underscoring the necessity for thorough analysis. These results carry important implications for developers, investors, and housing policy formulation, contributing significantly to understanding Poland's residential real estate market dynamics and supporting further research and strategic real estate industry planning.

Assessing the coherency of different El Niño events with vegetation health using time-series remote sensing data and wavelet coherency analysis in part of Southeast Asia

Assessing the coherency of different El Niño events with vegetation health using time-series remote sensing data and wavelet coherency analysis in part of Southeast Asia

Time-frequency co-movements between climate uncertainty and carbon market returns: Evidence based on wavelet coherence analysis

Time-frequency co-movements between climate uncertainty and carbon market returns: Evidence based on wavelet coherence analysis

Is hotel revenue performance effective for destination competitiveness? An assessment by wavelet coherence analysis

Is hotel revenue performance effective for destination competitiveness? An assessment by wavelet coherence analysis

Analysis of Spatiotemporal Variation in Precipitation on the Loess Plateau from 1961 to 2016

This study utilized annual precipitation data collected from 76 meteorological stations located on the Loess Plateau and its adjacent regions. It employed empirical orthogonal function (EOF) analysis, the Mann–Kendall trend test (M-K), and continuous wavelet transform (CWT) to investigate the spatial distribution patterns, temporal trends, and periodicity of annual precipitation from 1961 to 2016. The results showed the following: (1) The long-term averages of annual rainfall on the Loess Plateau exhibited a general decline from the southeast to the northwest, with certain areas demonstrating a trend of reduction radiating outward from the central region. This precipitation regime was fundamentally governed by the interplay between geographic coordinates and topo-graphical characteristics. Nevertheless, this spatial distribution pattern is expected to undergo changes in the future. (2) Annual precipitation in the southern and eastern parts decreased significantly, while the western part reported the greatest increase, and thus the spatial variability of precipitation will decrease in the future. (3) Annual precipitation on the Loess Plateau generally has a period of about 4 years. The wavelet coherence analysis reveals that El Niño events, occurring over a brief 4-year interval, correlate with diminished precipitation patterns across the eastern and southern sectors of the Loess Plateau, consequently attenuating the precipitation’s spatial variability throughout the entire geographical domain. Therefore, in the future, when El Niño occurs, it is necessary to prevent droughts in the eastern and southern regions of the Loess Plateau.

Hybrid Deep Learning Model for Fault Diagnosis in Centrifugal Pumps: A Comparative Study of VGG16, ResNet50, and Wavelet Coherence Analysis

Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification in CPs, integrating Wavelet Coherence Analysis (WCA) with deep learning architectures VGG16 and ResNet50. WCA is initially applied to vibration signals, creating time–frequency representations that capture both temporal and frequency information, essential for identifying subtle fault characteristics. These enhanced signals are processed by VGG16 and ResNet50, each contributing unique and complementary features that enhance feature representation. The hybrid approach fuses the extracted features, resulting in a more discriminative feature set that optimizes class separation. The proposed model achieved a test accuracy of 96.39%, demonstrating minimal class overlap in t-SNE plots and a precise confusion matrix. When compared to the ResNet50-based and VGG16-based models from previous studies, which reached 91.57% and 92.77% accuracy, respectively, the hybrid model displayed better classification performance, particularly in distinguishing closely related fault classes. High F1-scores across all fault categories further validate its effectiveness. This work underscores the value of combining multiple CNN architectures with advanced signal processing for reliable fault diagnosis, improving accuracy in real-world CP applications.

Time-Varying Impacts of Monetary Policy and Socio-Economic Factors on China’s CO2 Emissions and Ecological Footprint: A Multi-Methodological Analysis

This research explores the least explored domain concerning the impact of monetary tactics on carbon dioxide emissions in China, thereby adding depth to environmental economics. The analysis spans 1982–2022 and explores the interplay between monetary instruments, ecological footprint, CO2 emissions, and factors like human capital, GDP per capita, remittances, urbanization, and fossil fuel dependence. The investigation utilizes a multifaceted approach, including wavelet analysis techniques, like wavelet power spectrum, wavelet coherence analysis, quantile on quantile, and quantile regression. The findings unveil critical insights. The results demonstrate that monetary policy has had differing effects on emissions depending on the time. Contractionary policies are good for lowering medium- and long-term emissions, even if they cause a short-term increase in emissions. Furthermore, the study emphasizes the beneficial impact of human capital development on CO2 emissions. Fossil fuels, per capita income, and population significantly contribute to environmental damage beyond monetary policy. This research contributes original insights by integrating monetary policy and socio-economic factors in a comprehensive, multi-methodological framework, offering valuable guidance for crafting policies that balance economic growth with environmental sustainability.

Roles of ecological and hydrological processes in the variability of carbon fluxes in a salt marsh of the Yangtze Estuary: Model simulations vs. measurements

Carbon exchange in coastal wetlands is a complex biochemical process regulated by a combination of meteorological, plant-soil, and hydrological factors. In the Yangtze Estuary, a process-based carbon flux model was developed to further elucidate the spatiotemporal dynamics of carbon fluxes of different marsh species and elevations, incorporating the roles of climatic, hydrological, and geographical factors. The model was validated based on measurements of CO2 and CH4 fluxes using the static chamber and eddy covariance methods. The results showed that the model reproduced well the time series of the net ecosystem change (NEE) and CH4 flux from diel to half-month scales. During tidal cycling, the ecosystem CO2 and CH4 fluxes were suppressed under inundation, and the magnitude of suppression was proportional to the water depth. The landform of the tidal flat determined the submerged height and duration, and the hydrological effects on carbon fluxes varied with elevation. The model also reasonably reflected the negative correlation between tidal height and salinity and carbon fluxes during the spring-neap cycle. Sensitivity tests revealed that temperature and tidal height were the two most critical factors affecting the carbon fluxes. Wavelet coherence analysis further indicated that temperature and tidal height explained most of the periodic variations in NEE at the diel scale. Specifically, temperature dominated CH4 emission dynamics in spring and winter, whereas tides replaced temperature as the dominant factor in summer. The temperature also exhibited a stronger coherent intensity at high elevations, whereas the influence of tides was greater in low-lying regions. This study emphasizes the associated effects of climatic, biological, and hydrological factors on the spatiotemporal heterogeneity of carbon fluxes in the coastal marshes. This coupled model is expected to be beneficial for estimating the carbon sequestration capacity in China's coastal salt marshes under global change conditions.

Unravelling the impact of clean energy on the tourism sector of the stock market: Evidence from quantile granger causality and wavelet coherence analysis

Unravelling the impact of clean energy on the tourism sector of the stock market: Evidence from quantile granger causality and wavelet coherence analysis

The Role of Sustainable Competitiveness Indicator in Malaysian Tourism and Economic Growth

Abstract This paper proposes a sustainable competitiveness indicator for the state of tourism vulnerability in the Malaysian tourism market. Further investigation has also been done on the time-frequency and lead-lag relationship of the indicator with tourism development and economic growth. The resultant indicator is consisted of 12 variables, identified based on the pillars of sustainability and competitiveness to extract a common vulnerability component using a dynamic approximate factor model. The model’s accuracy rate was at a promising 92% based on the variable importance assessment using random forest algorithms. Through the implementation of bivariate wavelet coherence analysis, the empirical results indicated that the constructed indicator has a positive leading role in Malaysian tourism development and economic growth. This indicator can be embedded within an early warning system to signify vulnerabilities in the Malaysian tourism market.

The fluctuation characteristics of typical lakes in arid areas

Study regionThree typical lakes in Xinjiang, Ulungur Lake is a fractured lake, located in the north of the Junggar Basin; China's largest inland freshwater throughput lake named Bosten Lake; known for Barkol Lake’s salt content, it is also a closed inland lake. Study focusThe study aims to investigate and contrast the spatio-temporal variation characteristics of three lake types in Xinjiang and to evaluate the accuracy of four water body indices: NDWI, MNDWI, AWEI, and MAWEI. These indices reflect water body information by calculating differences in reflectivity. The research analyzes spatial and temporal variations of these typical Xinjiang lakes from 2001 to 2022 and explores the relationship between meteorological elements and lake regulation. New hydrological insights for the regionThe study examined the relationship between typical Xinjiang lakes and meteorological elements using wavelet coherence analysis and cross wavelet transform. The findings indicate that: 1) Among the three lakes, Ulungur Lake's area variation is most directly influenced by its average air temperature, evaporation, and precipitation. 2) In the semi-arid region, temperature significantly impacts the fluctuations of Bosten and Ulungur Lakes. Temperature affects Bosten Lake the most, accounting for 55.53 %, while Ulungur Lake is also primarily affected by temperature, accounting for 58.71 %. Barkol Lake is mostly influenced by precipitation, which accounts for 35.95 %. 3) Temporally, Bosten Lake exhibits the largest area change range, whereas Ulungur Lake shows the smallest. Spatially, Barkol Lake experiences the most significant changes, and Bosten Lake undergoes the least. This suggests that ERA5 data can be utilized for regional meteorological and hydrological trend studies, and the Google Earth Engine (GEE) platform's long-term water change monitoring capabilities may support sustainable development management of inland lakes.

Dynamic relationships between inflation, industrial production, and freight rates in the EU: A wavelet coherence and causality analysis

This study investigates the impact of maritime transport costs on macroeconomic indicators in the European Union (EU) economy. Using data for the period between June 2002 and April 2024, the dynamic relationships between the Baltic Dry Index, the Baltic Dirty Tanker Index, and the Baltic Clean Tanker Index and EU inflation and industrial production are analyzed using wavelet coherence analysis and the Breitung-Candelon frequency domain causality test. The results show that maritime transport costs significantly impact EU inflation and industrial production, especially during periods of economic crisis and shocks. The study suggests that the EU needs to strengthen its supply chain management, diversify its logistics infrastructure, and develop strategies to reduce energy costs. Proactive and sustainable policy recommendations for the EU are also presented to increase the predictability of maritime transport costs. Our study emphasizes that the EU needs to make structural investments in energy and logistics to ensure price stability. It suggests that the EU should monitor and integrate transport costs into economic policies.

Effects of economic growth, foreign direct investment, energy consumption, and urbanization on the ecological footprint in Vietnam

This research examines the impacts of economic growth, foreign direct investment, energy consumption, and urbanization on the ecological footprint in Vietnam from 1992 to 2022. The Wavelet Coherence analysis method is employed to evaluate the correlation levels between variables across various periods and frequencies, providing insights into the mechanisms and extent to which these factors influence the environment amidst urbanization and economic development. The research results indicate a robust in-phase relationship among energy consumption, urbanization, and the ecological footprint. Meanwhile, the interaction between economic growth, foreign direct investment, and the ecological footprint is observed to be more intricate and fluctuates over time. Drawing from these insights, the authors recommend adopting targeted sustainable development policies, promoting clean energy usage, and implementing effective urban management strategies to mitigate the adverse environmental impacts of economic growth in Vietnam.

The resilience dynamics of energy ETF accessibility and stock market sentiment in China during the post-pandemic era

This paper investigates the dynamic interaction between energy ETF accessibility and stock market sentiment through the lens of resilience during the post COVID-19 pandemic period. We find that they tend to positively co-move in the long run. Adverse exogenous shocks on one of them not only have a persistent impact on itself but also cause non-diminish damage to the other. Compared to stock market sentiment resilience, energy ETF accessibility resilience is more constrained by the long-run co-movement. The co-movement is not significantly reshaped by possible structural time-breaks or Markovian regime shifts. Further exploration based on wavelet coherence analysis provides evidence for a stable and even gradually strengthening positive coherence between the two types of resilience in a volatile environment. Accordingly, our findings can offer valuable insights for financial regulators to achieve their designed objectives in the secondary markets of energy and stocks.

Impact of oil and gold prices on Bitcoin price during Russia-Ukraine and Israel-Gaza wars

Political tensions between countries and geopolitical events can create significant uncertainty in financial markets, impacting the prices of traditional assets such as oil and gold, as well as the emerging asset, Bitcoin. This study investigates the effect of gold and oil prices, including their price shocks, on Bitcoin price during two specific conflicts: the Russia-Ukraine war and the Israel-Gaza conflict. To achieve this, we employed the Vector Auto-Regression (VAR) method and wavelet coherence analysis. The data used spans daily observations from January 2022 to April 2024.The findings indicate that gold prices have a positive impact on Bitcoin prices. Specifically, there is a predominantly short-term relationship between gold prices and Bitcoin prices throughout the entire period under review, with this influence gradually decreasing over the medium and long term. Conversely, the relationship between Brent oil prices and Bitcoin prices during the same period shows that crude oil prices generally have a negative and insignificant effect on Bitcoin prices in the short term.

Forecasting Equity Premium in the Face of Climate Policy Uncertainty

ABSTRACTThis study examines the role of the US climate policy uncertainty (CPU) index in forecasting the equity premium, employing shrinkage methods such as LASSO and elastic net (ENet) to dynamically select predictors from a dataset spanning April 1987 to December 2022. Alongside CPU, other uncertainty predictors like economic policy uncertainty (EPU), geopolitical risk (GPR), and the volatility index (VIX) are considered to assess their complementary roles in out‐of‐sample (OOS) equity premium forecasting. The results reveal that while CPU alone cannot consistently predict the equity premium, it provides crucial complementary information when combined with other predictors, leading to a statistically significant OOS of 1.231%. The relationship between CPU and the equity premium is time varying, with a stronger influence observed during periods of economic downturn or heightened uncertainty, as demonstrated by wavelet coherence analysis. This study also identifies CPU's significant impact on industry‐specific returns, particularly in climate‐sensitive sectors, offering valuable insights for investment strategies and risk management in an era of increasing CPU.

Reconstructed centennial precipitation-driven water storage anomalies in the Nile River Basin using RecNet and their suitability for studying ENSO and IOD impacts

While the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GFO) missions have offered valuable observations for monitoring total water storage anomalies (TWSA), their short record constrains our ability to study the complete range and long-term variability of TWSA in the Nile River Basin (NRB). Previous studies reconstructing TWSA in this region either relied on specific hydrological models or did not consider spatial correlations among the TWSA grids. Here, we employ RecNet, a deep learning model capable of providing independent TWSA observations without relying on hydrological models while considering spatial correlations, to reconstruct precipitation-driven TWSA in the NRB from 1923 to 2022. The reconstructed data are validated by comparisons with the Global Land Data Assimilation System (GLDAS), the WaterGAP Global Hydrology Model (WGHM), the water balance budget, long-term runoff data, and GRACE-REC (i.e., a global reconstruction dataset freely available online). Subsequently, the suitability of the reconstructed data for studying El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) impacts within the NRB is assessed. Dividing the NRB into four sub-regions, i.e., the Lake Victoria Basin (LVB), the Bahr el Jebel and Bahr el Ghazal basins (BJBG), Ethiopian Highlands region (EH), and the Lower Nile River Basin (LNRB), it is shown that RecNet successfully reconstructs precipitation-driven TWSA over BJBG and EH, achieving correlation coefficient (CC), normalized root mean square error (NRMSE), and Nash–Sutcliffeefficiency (NSE) of 0.94/0.11/0.88 and 0.96/0.09/0.91 during the testing period, respectively. Additionally, RecNet’s reconstruction shows better agreement with GLDAS and WGHM than GRACE-REC, correlating well with runoff and the water balance budget in these regions. The relatively poor performance in the LVB and LNRB regions could be attributed to the substantial influence of Lake Victoria and the arid climate, respectively. Correlation analysis and wavelet coherence analysis identify significant coherence between ENSO/IOD and the reconstructed TWSA in BJBG and EH, with CC values of −0.68/0.34 and −0.82/0.56, respectively. This study provides centennial reconstructed TWSA data that could be useful in climate change/variability studies and water resource management within the NRB.