2020
DOI: 10.1016/j.eneco.2020.104835
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Dynamic co-movement between oil and stock markets in oil-importing and oil-exporting countries: Two types of wavelet analysis

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Cited by 132 publications
(64 citation statements)
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“…First, an essential characteristic of the wavelet method is to capture latent processes with time varying cycle trends, lead-lag interactions, and patterns in the underlying time series ( Fakhfekh et al, 2021 ; Sherif, 2020 ). Second, a major advantage of wavelet coherence approach relates with its flexibility in dealing with non-stationary signals which are prevalent in the prices of soft commodities ( Aguiar-Conraria and Soares, 2011 ; Demir et al, 2020 ; Jiang and Yoon, 2020 ; León and Soto, 1997 ; Oglend and Asche, 2016 ; Shehzad et al, 2021 ). In addition, the wavelet coherence framework is also useful to study the co-movements between time series variables with frequent structural changes ( Fruehwirt et al, 2021 ; Kristoufek et al, 2016 ), and the variables that face sudden fluctuations in their structure.…”
Section: Data and Research Methodologymentioning
confidence: 99%
“…First, an essential characteristic of the wavelet method is to capture latent processes with time varying cycle trends, lead-lag interactions, and patterns in the underlying time series ( Fakhfekh et al, 2021 ; Sherif, 2020 ). Second, a major advantage of wavelet coherence approach relates with its flexibility in dealing with non-stationary signals which are prevalent in the prices of soft commodities ( Aguiar-Conraria and Soares, 2011 ; Demir et al, 2020 ; Jiang and Yoon, 2020 ; León and Soto, 1997 ; Oglend and Asche, 2016 ; Shehzad et al, 2021 ). In addition, the wavelet coherence framework is also useful to study the co-movements between time series variables with frequent structural changes ( Fruehwirt et al, 2021 ; Kristoufek et al, 2016 ), and the variables that face sudden fluctuations in their structure.…”
Section: Data and Research Methodologymentioning
confidence: 99%
“…Wavelet analysis is one of the most commonly used frequency analysis in non-stationary time series data analysis. The advantage of the wavelet analysis relates to its flexibility in the use of various non-stationary signals ( Aguiar-Conraria and Joana Soares, 2011 ; Bilgili, 2015 ; Bravo et al, 2020 ; Jiang and Yoon, 2020 ; Kuşkaya and Bilgili, 2020 ; Tiwari et al, 2020 ; Bilgili et al, 2020a , 2020b ). As wavelets are structured over finite intervals of time and are not perforce homogeneous over time, they are localised in time scale ( Ramsey, 2014 ).…”
Section: Methodology and Datamentioning
confidence: 99%
“…On the wavelet coherence plots, the black contour shows the 5% significance level, and regions with strong co-movements are represented by warmer colors (red), whereas colder colors (blue) represent regions with weak co-movements. The arrows provide the direction of interdependence and causality relationships ( Torrence and Webster, 1999 ; Tiwari, 2013 ; Yang et al., 2017 ; Pal and Mitra, 2019 ; Jiang and Yoon, 2020 ). Arrows pointing to the right( → ) indicate that EPU and the sector volatility are positively correlated.…”
Section: Empirical Analysismentioning
confidence: 99%