Wavelet Coherence Approach Research Articles

Investigating Fisher effect in SACU countries: A wavelet coherence approach

Investigating Fisher effect in SACU countries: A wavelet coherence approach

The Impact of COVID-19 on the Relationship between Non-Renewable Energy and Saudi Stock Market Sectors Using Wavelet Coherence Approach and Neural Networks

In this study, we examine the impact of COVID-19 on the relationship between non-renewable energy and Saudi stock market sectors for the period 11 January 2017–22 January 2022. We apply wavelet coherence and Radial Basis Function Neural Network (RBFNN) models. Our results provide evidence that COVID-19 led to an increase in the strength of the relationship between oil as a main non-renewable energy source and Saudi stock market sectors and affected the nature and direction of this relationship. The relationships between oil and commercial and professional services, materials, banks, energy, and transportation sectors are the most affected. Our results will help hedge funds, mutual funds, and individual investors, forecast the direction of Saudi stock market sectors and the use of oil for hedging or diversification during periods of uncertainty and crisis. It will also help decision and policymakers in Saudi Arabia to make the necessary decisions and actions to maintain the stability of the stock market sectors during these periods.

The Impact of the Ukrainian War on Stock and Energy Markets: A Wavelet Coherence Analysis

This study attempts to examine the existence of interdependencies between specific stock market indices, exchange rates and crude oil for the period January 2021 to July 2022 with daily data. In the period we have chosen, the post-vaccination phase against COVID-19, as well as the war in Ukraine, is covered. The variables selected for this study are RTSI, Eurostoxx, S&P 500, EUR/USD and RUB/USD exchange rates and crude oil prices. The selection of the specific variables was made because they are directly related to the pre-war period that coincides with the post-vaccine period of the pandemic, which allowed us to characterize it as the normal period and to characterize the period of the war in Ukraine that coincides with the energy crisis as the unstable period. In this way, the present study covers the markets of Russia and other developed economies. For empirical purposes, we applied a wavelet coherence approach in order to investigate the possible existence of simultaneous coherence between the variables at different times and scales for all the considered times. The findings of the study reveal the existence of strong correlations between all variables, during different time periods and for different frequencies during the period under review. Of particular interest is the finding that shows that during the crisis period, the RTSI significantly affects both the European and American stock markets, while also determining the evolution of the Russian currency. In addition, it appears that capital constraints in the Russian stock market, combined with increased demand for crude oil, determine the interdependence between RTSI and crude oil. Finally, an interesting finding of the study is the existence of a negative correlation between the US stock index and crude oil in low-frequency bands and the RTSI and Eurostoxx with crude oil for the post-vaccination and pre-war periods in the medium term. These findings can be used by both investors and portfolio managers to hedge risks and make more confident investment decisions. In addition, these findings can be used by policy makers in the planning of regulatory policies regarding the limitations of the systemic risks in capital markets.

Socio-economic and technological drivers of sustainability and resources management: Demonstrating the role of information and communications technology and financial development using advanced wavelet coherence approach

Inclusive and sustainable growth can only be ensured through socio-economic and technological transformation. In this regard, this study offers a new perspective on the dynamic interrelationship between financial development (FD), economic growth (GDP), information and communication technologies (ICT), education (EDU), and CO2 emissions (CO2) in the United Kingdom. The paper employs the Breitung and candelon (2006) causality and wavelet approaches to assess these connections using data from 1990Q1 to 2019Q4. The wavelet coherence is employed to explore the coherence and lead/lag interrelationship between CO2 and the independent variables over dissimilar time scales. Finally, the Breitung and candelon (2006) causality test is used as a robustness check for the wavelet coherence approach. The wavelet coherence (WTC) revealed: (a) negative coherence between CO2 and GDP with GDP leading; (b) negative co-movement between CO2 and financial development with financial development leading; (c) positive co-movement between CO2 and energy use with energy use leading; (d) negative co-movement between ICT and CO2 with ICT leading; and (e) negative co-movement between education and CO2 with education leading. The outcomes of the multiple wavelet and partial wavelet as well as the Breitung and candelon (2006) causality comply with those of the wavelet coherence. Since a connection between CO2 and financial development and ICT is found in the short-medium term, our findings imply that policymakers should focus on investing in technologies and financial markets. In addition, the report recommends that emphasis be placed on green energy policies, as growing pollution levels from fossil fuels impede long-term productivity.

Interdependence structure of global commodity classes and African equity markets: A vector wavelet coherence analysis

Commodity and African stock markets are capable of producing safe returns when needed. However, the high commodity price volatility coupled with commodity financialisation suggests that the fundamental characteristics of commodity returns may change. The probable situation with commodity returns is no different from those of equity returns from Africa, owing to the new trade liberalisations between African markets which signal plausible high cross-market integration. These developments could render diversification extinct and warrant that timely assessments are made to ascertain the dynamic interdependencies between these markets. Using the vector wavelet coherence approach, we examine the multiple, quadruple, and n-dimensional interdependencies between the returns on global commodities and African equity markets. Our findings from a time-frequency paradigm divulge dynamic interdependence between the various commodities and equity returns from Africa. We report high interdependence in the short-term with the degree being commodity-dependent. Our analysis – which covers commodities from energies, precious metals, grains, and softs – produces findings that have significant implications for market regulation, asset allocation, and portfolio management.

Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin

The complicated phenomenon induced by inadequate precipitation is a drought that impacts water resources and human life. Traditional methods to assess groundwater drought events are hindered due to sparse groundwater observations on a spatio-temporal scale. These groundwater drought events are not well studied in the study area of the Indus Basin Irrigation System (IBIS) holistically. This study applied four machine learning models to the training datasets of Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and Groundwater Storage (GWS) data to improve resolution to 0.25° from 1°. The Extreme Gradient Boosting (XGBoost) model outperformed the four models and results showed Pearson correlation (R) (0.99), Nash Sutcliff Efficiency (NSE) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm). The GRACE Groundwater Drought Index (GGDI) was calculated by normalizing XGBoost-downscaled GWS. The trend characteristics, the temporal evolution, and spatial distribution of GGDI were analyzed across the IBIS from 2003 to 2016. The wavelet coherence approach was used to evaluate the relationship between teleconnection factors and GGDI. The XGBoost downscaling model can accurately reproduce local groundwater behavior, with the acceptable correlation of coefficient values for validation (ranging from 0.02 to 0.84). The accumulated Standardized Precipitation Evapotranspiration Index (SPEI) with the time of 1, 3, and 6 months, and self-calibrated Palmer Drought Severity Index (sc-PDSI) were used to validate GGDI. The findings have demonstrated that GGDI has comparable drought patterns to SPEI-3 and SPEI-6 and sc-PDSI. The teleconnection factors have a significant impact on the GGDI shown by the wavelet coherence technique. The impact of the sea surface temperature index (namely, NINO3.4) on GGDI was observed significantly high among other teleconnection factors in the IBIS. The proposed framework can serve as a useful tool for drought monitoring and a better understanding of extreme hydroclimatic conditions in the IBIS and other similar climatic regions.

Geopolitical risk and the returns and volatility of global defense companies: A new race to arms?

The ongoing war in Ukraine and rising geopolitical tensions between Russia and western countries have led several European countries to increase defense spending to historic levels. Under such intense circumstances, we examine the co-movements between the daily geopolitical risk (GPR) index and the daily returns and volatility of 36 global defense and aerospace companies covering ten countries and three continents. Using the wavelet coherence approach, we find significant co-movement concentrated around the eruption of the war in Ukraine, mostly for medium and longer scales, indicating a flight-to-arms phenomenon. The strong co-movement is significant for most of US and European companies. Specifically, the GPR index leads the return and volatility of several US and European companies at medium and longer scales throughout the war period, and its impact is mostly positive. Our findings offer new and interesting implications for multi-horizon market participants such as traders and investors.

Rare Earth Market, Electric Vehicles and Future Mobility Index: A Time-Frequency Analysis with Portfolio Implications

This study investigates the co-movements between the Solactive Electric Vehicle and Future Mobility Index (EVFMI) and multiple rare earth elements (REEs). We applied a TVP-VAR model and bivariate wavelet coherence approach to capture co-movements both in the time and frequency domain considering short-, medium- and long-term investment horizons. Using daily returns from 1 June 2012 to 4 June 2021, the results of the TVP-VAR model show that individual REEs and the EVFMI have strong return connectedness and are heterogenous over time. The bivariate wavelet coherence approach reveals that Dysprosium, Neodymium, Praseodymium and Terbium returns have positive co-movement (in-phase) with the EVFMI in the medium-term and long-term. In contrast, Cerium, Europium, Lanthanum and Yttrium returns have negative co-movements (out-phase) with the EVFMI in the medium-term and long-term. We find strong positive co-movements between the MVIS Global Rare Earth/Strategic Metals Index (MVREMX) and EVFMI at multiple wavelet scales. Following the lead/lag relationship, Cerium, Europium and Lanthanum, Yttrium returns are leading the EVFMI, and Neodymium, Dysprosium, Praseodymium, Terbium and MVREMX returns are lagging to the EVFMI. This study, therefore, suggests heterogenous hedging and diversification properties of REEs over time and investment horizons. Specifically, Cerium, Europium, Lanthanum and Yttrium act as strong hedges in long-term investment horizons and Neodymium, Dysprosium, Praseodymium and Terbium are weak hedges or diversifiers in short-term investment horizons. These results may be of particular interest to investors and relevant to policymakers considering multiple investment horizons.

Economic performance and natural resources commodity prices volatility: Evidence from global data

The current research study aims to investigate natural resources, commodity prices volatility and global economic performance covering the period from 1970 to 2021. This study uses various variables, i.e., total natural resources volatility (TNR), crude oil price (CR), oil rents (OR), and natural gas rents (NGR), to measure natural resources price volatility. This study uses the wavelet approach for empirical investigation of causal association in the short and long run. The empirical estimations of the wavelet power spectrum reveal that all the variables are vulnerable, specifically in global crisis events. Besides, the wavelet coherence approach indicates a bidirectional causal association exists between natural resources commodity price volatility (TNR, OR, NGR) and global economic performance. However, the causal impact of natural resources price volatility is found only in the short-run, while economic performance significantly causes volatility in natural resources price in the long run. Moreover, the wavelet coherence authenticates unidirectional causal association running from global economic performance to CR in both the short-run and long-run. This study provides some policy insights which could have essential economic and natural resources price volatility control measures for policy-makers and future researchers.

Energy security versus food security: An analysis of fuel ethanol- related markets using the spillover index and partial wavelet coherence approaches

The recent surge in biofuel production has caused concern about energy and food security. Most of the literature related to the “fuel vs. food” issue has paid close attention to futures markets, in which commodity price volatilities are greatly increased by the many non-commercial traders that are responsible for differences in price movements between financial and non-financial markets, such as retail, wholesale, and farm-gate markets. This analysis focused on the wholesale and producer prices of fuel ethanol, gasoline, and corn to analyze the interrelations among the three commodity markets from January 2001 to December 2020. We applied the spillover index and partial wavelet coherence methods, which have rarely been used in this research area, and obtained results that were largely consistent. Our primary findings are as follows: First, gasoline and ethanol price returns tend to be positively correlated with the corn price return in the short term. Second, the price return of gasoline is positively related to the corn price return in the medium and long terms, while the price return of ethanol is negatively related to the corn price return in the medium term. Finally, there is evidence that the price returns of gasoline positively lead the price return in all the frequency domains. The results suggest that while biofuel policy in the US enhances the country's energy independence, the strategy adversely affects the access, stability, and availability of the country's food security. The repeal of the US Energy Policy Act and Energy Independence and Security Act would bring about a double dividend: higher efficiency of resource allocation and improved food security.

The outbreak of COVID-19 and stock market liquidity: Evidence from emerging and developed equity markets

The outbreak of the novel corona virus has heightened concerns surrounding the adverse financial effects of the outbreak on stock market liquidity and economic policies. This paper contributes to the emerging strand of studies examining the adverse effects of the virus on varied aspect of global markets. The paper examines the causality and co-movements between COVID-19 and the aggregate stock market liquidity of China, Australia and the G7 countries (Canada, France, Italy, Japan, Germany, the UK and the US), using daily three liquidity proxies (Amihud, Spread and Traded Value) over the period December 2019 to July 2020. Our empirical analysis encompasses wavelet coherence and phase-differences as well as a linear Granger causality test. Linear causality test results suggest that a causal relationship exists between the number of cases of COVID 19 infections and stock market liquidity. To quantitatively examine the degree of causality between COVID-19 outbreak and stock market liquidity, we employ the continuous wavelet coherence approach with results revealing the unprecedented impact of COVID-19 on stock market liquidity during the low frequency bands for countries that were hard hit with the COVID-19 outbreak, i.e., Italy, Germany, France, the UK and the US. Further, evidence shows that there is a heterogeneous lead-lag nexus across scales for the entire period of the study.

TIME AND FREQUENCY DEPENDENCY OF FOREIGN EXCHANGE RATES AND COUNTRY RISK: EVIDENCE FROM TURKEY

This study examines the time and frequency dependency nexus between foreign exchange (FX) rates and country risk in Turkey. We considered Turkey because it is a negative outlier country in terms of the progress of these indicators. Using quarterly data from 1990/Q1 to 2018/Q4 and the Wavelet Coherence approach, we find that an increase in the country risk causes an increase in the FX rates at different frequencies, especially in the medium and long term and different periods. The results highlight the significance of country risk for the progress of the FX rates. Policy implications are discussed.

Exploring the co-movements between COVID-19 pandemic and international air traffic: A global perspective based on wavelet analysis.

The travel and tourism industry was one of the fastest‐growing industries before the onset of the COVID‐19 pandemic. However, to avoid COVID‐19 spread, the government authorities imposed strict lockdown and international border restrictions except for some emergency international flights that badly hit the travel and tourism industry. The study explores the nexus between international air departures and the COVID‐19 pandemic in this strain. We use a novel wavelet coherence approach to dissect the lead and lag relationships between international flight departures and COVID‐19 deaths from January 2020 to September 2020 (COVID‐19 first wave period). The results reveal that international flights cause the spread of COVID‐19 spread during May 2020 to June 2020 worldwide. The overall findings suggest asymmetries between daily international flight departures and COVID‐19 deaths globally at different time‐frequency periods due to uncertainty surrounding the COVID‐19 pandemic. The study will be conducive for the policymakers to control the upsurge of COVID‐19 spread worldwide.

Do stock market fear and economic policy uncertainty co-move with COVID-19 fear? Evidence from the US and UK

PurposeThe purpose of the paper is to investigate co-movement of major implied volatility indices and economic policy uncertainty (EPU) indices with both the health-based fear index and market-based fear index of COVID-19 for the USA and the UK to help investors and portfolio managers in their informed investment decisions during times of infectious disease spread.Design/methodology/approachThis study uses wavelet coherence approach because it allows to observe lead–lag nonlinear relationship between two time-series variables and captures the heterogeneous perceptions of investors across time and frequency. The daily data used in this study about the USA and the UK covers major implied volatility indices, EPU, health-based fear index and market-based fear index of COVID-19 for both the first and second waves of COVID-19 pandemic over the period from March 3, 2020 to February 12, 2021.FindingsThe results document a strong positive co-movement between implied volatility indices and two proxies of the COVID-19 fear. However, in all the cases, the infectious disease equity market volatility index (IDEMVI), the COVID-19 proxy, is more representative of the stock market and exhibits a stronger positive co-movement with volatility indices than the COVID-19 fear index (C19FI). This study also finds that the UK’s implied volatility index weakly co-moves with the C19FI compared to the USA. The results show that EPU indices of both the USA and the UK exhibit a weak or no correlation with the C19FI. However, this study finds a significant and positive co-movement of EPU indices with IDEMVI over the short horizon and most of the sampling period with the leading effect of IDEMVI. This study’s robustness analysis using partial wavelet coherence provides further strengths to the findings.Research limitations/implicationsThe investment decisions and risk management of investors and portfolio managers in financial markets are affected by the new information on volatility and EPU. The findings provide insights to equity investors and portfolio managers to improve their risk management practices by incorporating how health-related risks such as COVID-19 pandemic can contribute to the market volatility and economic risks. The results are beneficial for long-term equity investors, as their investments are affected by contributing factors to the volatility in US and UK’s stock markets.Originality/valueThis study adds following promising values to the existing literature. First, the results complement the existing literature (Rubbaniy et al., 2021c) in documenting that type of COVID-19 proxy matters in explaining the volatility (EPU) relationships in financial markets, where market perceived fear of COVID-19 is appeared to be more pronounced than health-based fear of COVID-19. Second, the use of wavelet coherence approach allows us to observe lead–lag relationship between the selected variables, which captures the heterogeneous perceptions of investors across time and frequency and have important insights for the investors and portfolio managers. Finally, this study uses the improved data of COVID-19, stock market volatility and EPU compared to the existing studies (Sharif et al., 2020), which are too early to capture the effects of exponential spread of COVID-19 in the USA and the UK after March 2020.

CO-MOVEMENT OF NON-PERFORMING LOANS AND BANK CONCENTRATIONS IN AN EMERGING MARKET: A WAVELET COHERENCE APPROACH

This study analyzes the link between non-performing loans (NPL) and bank concentrations in Turkey using the wavelet coherence approach. In line with the expectation of the present study, there were fluctuations in NPL by 5 and 7 banking concentrations between 2000 and 2003. Moreover, NPL ratios in the long term cause 5 and 7 banking concentrations. It is concluded that NPL in the Turkish banking sector is active on the size of asset concentration ratios of the largest 5 and 7 banks. Additionally, there is a positive correlation between NPL and 5 and 7 bank concentrations during the domestic banking crisis.

The price volatility of natural resource commodity and global economic policy uncertainty: Evidence from US economy

Since the beginning of the 21st century, natural disasters and global economic shocks have affected economies in various aspects. This study investigates the causal relationship between the price volatility of natural resource commodity and global economic policy uncertainty in the US by using monthly data for the period of November 01, 2007–May 01, 2021. The wavelet power spectrum and wavelet coherence approaches are employed and the causal association is observed. In addition, the estimated results reveal that economic policy uncertainty and the price volatility of natural resource are more vulnerable than industrial production in the selected time span. Furthermore, the wavelet coherence illustrates a bidirectional causal association among global economic policy uncertainty, the price volatility of natural resource commodity and industrial production. However, a one-way causal association is observed running from industrial production to the price volatility of natural resource commodity. Moreover, the frequency domain causality validates the variables' short-run, medium-run, and long-run causal factors. The empirical findings offer several prominent policy implications which could be efficient from the long-run policy perspective.

A wavelet coherence approach to detecting ecosystem services trade-off response to land use change

Clarifying the complex land use impacts on ecosystem services (ESs) trade-off will be beneficial to watershed sustainable development, especially through scientific land use management and decision making. Dongting Lake Basin (DLB) is not only one of the most significant ecological barriers for the Yangtze River Economic Belt, but also an important grain production base of China. The trade-off between the grain production (GP) and water purification (WP) has become increasingly prominent. Here, we chose DLB as a case study area, applied spatial continuous wavelet transform and wavelet coherence analysis, characterized the ES trade-off intensity by wavelet coherence coefficient, and explored the influence of land use type, conflict and intensity on the trade-off between GP and WP. The results showed that the trade-off intensity between GP and WP in the DLB in 2015 had alleviated compared with 2005, and the coherence coefficient had increased while maitaining the negative value. The trade-off intensity was the strongest in farmland and forest land, and weaker in grassland and water body. The impact of land use conflict mainly depended on the specific types of land use conversions. For the transects where land use conversions mainly appeared between farmland and forest land, the intensification of land use conflict would increase the trade-off intensity (2005: R2 = 0.3862, p < 0.05; 2015: R2 = 0.2543, p < 0.05), while for the transects dominated by conversions to water body and grassland, stronger land use conflicts would reduce the trade-off intensity (2005: R2 = 0.3438, p < 0.05; 2015: R2 = 0.2668, p < 0.05). The impact of land use intensity was also realized through the land use type, with lower interpretation ratio. In addition, the wavelet coherence analysis showed that the scale about 10.51 km was the most suitable for exploring the trade-off between GP and WP, which was equivalent to the scale of the secondary watershed in the study area.

The role of tourism development in India's environmental degradation: evidence from ARDL and wavelet coherence approaches

PurposeThis empirical study explores the role of tourism development (TD) in India's environmental degradation. Since sustainable eco-tourism is essential for India, whose long-term economic prosperity also depends on robust tourism growth. Hence, this study offers specific policy proposals for sustainable tourism based on the simulated outcomes.Design/methodology/approachThe study employed the quarterly data from 1995Q1–2018Q4 for empirical validation. Moreover, the autoregressive distributed lag (ARDL) approach, Toda and Yamamoto (TY) causality model and wavelet coherence are also used to analyse the role of TD in India's environmental degradation.FindingsThe ARDL bounds test confirms the long-run relationship between the series. The long-run results from the ARDL model also indicate the driving role of TD in India's environmental degradation. In addition, the usage of the wavelet coherence method confirms the changes in TD that leads to changes in India's pollution level at different frequencies and periods, especially in the long run.Research limitations/implicationsSince this analysis is India-specific, these findings may lack generalizability to other developing economies. Therefore, researchers are encouraged to examine the impact of tourism growth on the natural environment in other countries either in a panel or time-series framework.Practical implicationsThis study suggests crucial implications for checking the pollution sands from TD without sacrificing tourism-led economic growth. This would be possible if the usage of green energy in India's transport sector is promoted.Originality/valueThis is the first study that analyzes the impact of TD on environmental degradation in the ARDL, wavelet coherence, TY frameworks for enabling the Indian economy for a sustainable tourism practice.

Does COVID-19 pandemic cause natural resources commodity prices volatility? Empirical evidence from China

COVID-19 pandemic caused havoc around the globe in both economic and non-economic sectors. This paper, unlike previous studies, evaluates the role of COVID-19 on the volatility in natural resources. The volatility of natural resources commodity prices has been the center of discussion, especially during the pandemic. Unlike previous studies, this study aims to evaluate the role of the pandemic, i.e., Covid-19 and its possible impact on volatility in natural resources commodity prices for China. China has been the center of this epidemic disease and is considered one of the major economies affected by the Covid-19; therefore, it is better to conduct this study for China. This study uses data from January 2020 till September 2021 to capture the peak time of Covid-19. Moreover, this study employs the novel wavelet power spectrum and wavelet coherence approach to better capture volatility within commodity prices volatility and Covid-19 and evaluate the association between both variables. The empirical results reveal that only natural resources commodity prices are volatile and only short. While Covid-19 positive cases and Covid-19 deaths are not vulnerable during the study period. Moreover, the wavelet coherence conforms that both Covid-19 positive cases and Covid-19 deaths significantly cause volatility in natural resources commodity prices. Although, volatility is found at different periods; still, volatility is observed only in the short-run. The study also provides relevant policy implications to ensure a relevant and timely solution for the existing issue. Moreover, future research guidelines and the study's limitations are also provided.

Exploring the possible linkage between the precipitation and temperature over Iran and their association with the large-scale circulations: Cumulative spectral power and wavelet coherence approaches

The current study aims at improving our common knowledge of precipitation and surface temperature variabilities in Iran across different time scales (from intra-annual to decadal) by using two methods (the cumulative spectral power and wavelet coherence analyses) and linking the variabilities to several teleconnection indices, namely Mediterranean Oscillation (MO), Quasi-biennial Oscillation (QBO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), and solar activity (~ Sunspot Number (SN)). Furthermore, the interdependence of the spectral components of indices is also explored. We divided the country to eleven climatic zones and performed the analysis for individual zones as well as for the total area by constructing the precipitation and temperature signals for the study period of 1979–2021 based on the gridded data. Based on the wavelet coherence analysis, the surface temperature and precipitation variations are strongly linked together, in particular at annual cycle and the phase status is predominantly anti-phase for all zones as well as for the all temporal scales. In addition, the MO can be regarded as a predictor of the annual variation of both variables over Iran as our results indicate a significant co-variability in the annual band between MO signal and these variables. Furthermore, a similar semi-annual component is detected in both MO and temperature signals with an anti-phase status, but the same component was not found in the precipitation. Other indices have also substantial influences on both temperature and precipitation, mostly at inter-annual and decadal periods. Based on the wavelet coherence and lagged correlation analyses, evidences of common periodicity, namely 11-year solar cycle, between SN and SOI signals is clearly revealed. Moreover, our results show that if the SN signal is considered as the cause and the SOI signal as the effect, the minimum value of the 11-year component of SOI occurs approximately 2.5 to 3 years after the maximum of the same component in the SN signal. By employing the cumulative spectral power analysis, the main quasi-periodic components of the climatic signals, the surface temperature, and precipitation with their power distribution are presented and compared in different frequency bands. The application of both methods on different zones over Iran is also addressed in addition to the whole area. The results show that although some indices have minor impact on the variabilities of the precipitation and temperature for the whole area in some frequency bands, but the large impact is found in a number of individual zones.