Multiscale coherence analysis of reference evapotranspiration of north-western Iran using wavelet transform

This study applies different wavelet coherence formulations for investigating the multiscale associations of reference Evapotranspiration (ET0) of Tabriz and Urmia stations in North West Iran with five climatic variables, mean temperature (T), pressure (P), relative humidity (RH), wind speed (U) and Solar Radiation (SR). The relationships between different variables are quantified using the Average Wavelet Coherence (AWC) and the Percentage of Significant Coherence (PoSC). The Bivariate Wavelet Coherence (BWC) analysis showed that mean temperature (AWC = 0.73, PoSC = 59.18%) and wind speed (AWC = 0.63, PoSC = 49.55%) are the dominant predictors at Tabriz and Urmia stations. On considering the Multiple Wavelet Coherence (MWC) analysis, it is noticed that among the two-factor combinations, the T-P and P-RH combinations resulted in the highest coherence values for Tabriz and Urmia stations. T-U-SR combination produced the highest multiple wavelet coherence values among the three-factor cases for both the stations. The Partial Wavelet Coherence (PWC) analysis indicated a drastic reduction in coherence from the values of respective BWC analysis, indicating a strong interrelationship between different variables and ET0. The interrelationship between meteorological variables and ET0 is more apparent at Tabriz, while it is controlled more by the local-scale meteorology at Urmia.

Time-Varying Nexus between Investor Sentiment and Cryptocurrency Market: New Insights from a Wavelet Coherence Framework

This study attempts to investigate the nexus between investor sentiment and cryptocurrencies prices. Our empirical investigation merges bivariate and multivariate wavelet tools to examine the investor sentiment nexus to inter-cryptocurrencies prices. The study outcomes show that the Sentix Investor Confidence index provides significant information in explaining long-term changes in Bitcoin and Litecoin prices. Moreover, the findings generated from the multiple wavelet coherence illustrate the simultaneous contribution of cryptocurrencies and the Sentix Investor Confidence index in explaining the Bitcoin index movement across frequencies and over horizons, especially during bubble burst periods. The study also suggests a time-dependent relationship of Bitcoin prices with alternative cryptocurrencies and the Sentix Investor Confidence index, mostly pronounced during the Bitcoin bubble. We discuss our results using GSV-based investor sentiment. Our findings remain robust and confirm the strong predictive power of investor sentiment in cryptocurrencies price movements over time and across scales.

The Imperativeness of Environmental Quality in China Amidst Renewable Energy Consumption and Trade Openness

It is widely accepted that CO2 emissions are the primary cause of climate change and environmental destruction. China, the world’s biggest carbon emitter, is the subject of this research. Utilizing the wavelet tools (wavelet correlation, wavelet coherence, multiple wavelet coherence, and partial wavelet coherence), the present study intends to capture the time-frequency dependence between CO2 emissions and renewable energy, economic growth, trade openness, and energy usage in China between 1965 and 2019. The advantage of the wavelet tools is that they can differentiate between short, medium, and long-run dynamics over the period of study. Furthermore, the study utilized the gradual shift causality test to capture the causal interconnection between CO2 emissions and the regressors. The findings from Bayer and Hanck showed a long-run relationship among the variables of interest. Furthermore, the findings from the wavelet coherence test revealed a positive relationship between CO2 emissions and economic growth and energy usage at all frequencies. Although there is a weak negative relationship between renewable energy and CO2 emissions in the short run, there is no significant co-movement between CO2 emissions and trade openness. The outcomes of the partial and multiple wavelet coherence also give credence to the outcomes of the wavelet coherence test. Lastly, the gradual shift causality test revealed a one-way causality from energy usage and economic growth to CO2 emissions. Based on the findings, suitable policy suggestions were proposed.

REVISITING THE “PURE” OIL-EXCHANGE CO-MOVEMENT FROM A TIME-DOMAIN PERSPECTIVE

In this paper, we examine the differences between CNY and other major currencies in coherence and the lead–lag relationship across the different time horizons to clarify whether crude oil, monetary factors, or both drive the movement of exchange rates. We employ partial and multiple wavelet coherence analyses to examine oil-exchange co-movement by excluding the influence of Federal Reserve System (FED) monetary policy — namely, the stance and uncertainty of monetary policy — and the difference in domestic and foreign monetary policy rates. Overall, we find that monetary easing by the FED is a major factor driving the co-movement. Specifically, after excluding the possible effects of monetary policy factors, the movement of the Euro exhibits the strongest and the Japanese yen the weakest dependence on crude oil price changes, whereas the British pound shows a moderate dependence. By contrast, the CNY shows strong co-movement with the crude oil price only over the long term implying the low degree of integration with the global markets. Our empirical results provide meaningful information for investors and policymakers.