Authors:
Andrii Bielinskyi
1
;
Vladimir Soloviev
1
;
Serhiy Semerikov
1
;
Victoria Solovieva
2
;
Andriy Matviychuk
3
and
Arnold Kiv
4
Affiliations:
1
Kryvyi Rih State Pedagogical University, 54, Gagarin av., Kryvyi Rih, Ukraine
;
2
State University of Economics and Technology, 16, Medychna str., Kryvyi Rih, Ukraine
;
3
Kyiv National Economic University named after Vadym Hetman, 54/1, Peremogy pr., Kyiv, Ukraine
;
4
Ben-Gurion University of the Negev, 653, P.O.B., Beer-Sheva, Israel
Keyword(s):
Crude Oil, Natural Gas, Sustainable Development, Multifractality, Multifractal Detrended Cross-Correlation Analysis, Cross-Correlations.
Abstract:
Regulatory actions aimed the sustainable development force ordinary traders, policymakers, institutional investors to develop new types of risk management strategies, seek better decision-making processes that would allow them more effectively reallocate funds when trading and investing in energy markets such as oil and gas. Due to their supply and demand, they are presented to non-equilibrium, chaotic, long-range dependent, etc. Oil and gas play an important role not only in the financial markets, but they are important in many goods and services, and their extensive usage leads to environmental damage. Thus, the dynamics of the corresponding energy-related indices is a useful indicator of the current environmental development, and their modeling is of paramount importance. We have addressed one of the methods of multifractal analysis which is known as Detrended Cross-Correlation Analysis (DCCA) and its multifractal extension (MF-DCCA) to get reliable and efficient indicators of cri
tical events in the oil and gas markets. For example, we have taken daily data of Henry Hub natural gas spot prices (US$/MMBTU), WTI spot prices (US$/BBL), and Europe Brent spot prices (US$/BBL) from 7 February 1997 to 14 December 2021. Regarding their (multifractal) cross-correlations, we get such indicators as the DCCA coefficient 𝜌 𝐷𝐶𝐶𝐴 , the cross-correlation Hurst exponent, the maximal, minimal, and mean singularity strength, the width of multifractality, and its asymmetry with the usage of sliding window approach. Our empirical results present that all of the presented indicators respond characteristically during crashes and can be effectively used for modeling current and further perspectives in energy markets and sustainable development indices.
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