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Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

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  • Wa̧torek, Marcin
  • Drożdż, Stanisław
  • Oświȩcimka, Paweł
  • Stanuszek, Marek

Abstract

Statistical and multiscaling characteristics of WTI Crude Oil futures prices expressed in US dollar in relation to the most traded currencies as well as to gold futures and to the E-mini S&P500 futures prices on 5 min intra-day recordings in the period January 2012–December 2017 are studied. It is shown that in most of the cases the tails of return distributions of the considered financial instruments follow the inverse cubic power law. The only exception is the Russian ruble for which the distribution tail is heavier and scales with the exponent close to 2. From the perspective of multiscaling the analysed time series reveal the multifractal organization with the left-sided asymmetry of the corresponding singularity spectra. Even more, all the considered financial instruments appear to be multifractally cross-correlated with oil, especially on the level of medium-size fluctuations, as the multifractal cross-correlation analysis carried out by means of the multifractal cross-correlation analysis (MFCCA) and detrended cross-correlation coefficient ρq show. The degree of such cross-correlations is however varying among the financial instruments. The strongest ties to the oil characterize currencies of the oil extracting countries. Strength of this multifractal coupling appears to depend also on the oil market trend. In the analysed time period the level of cross-correlations systematically increases during the bear phase on the oil market and it saturates after the trend reversal in 1st half of 2016. The same methodology is also applied to identify possible causal relations between considered observables. Searching for some related asymmetry in the information flow mediating cross-correlations indicates that it was the oil price that led the Russian ruble over the time period here considered rather than vice versa.

Suggested Citation

  • Wa̧torek, Marcin & Drożdż, Stanisław & Oświȩcimka, Paweł & Stanuszek, Marek, 2019. "Multifractal cross-correlations between the world oil and other financial markets in 2012–2017," Energy Economics, Elsevier, vol. 81(C), pages 874-885.
  • Handle: RePEc:eee:eneeco:v:81:y:2019:i:c:p:874-885
    DOI: 10.1016/j.eneco.2019.05.015
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    Citations

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    Cited by:

    1. Stanisław Drożdż & Ludovico Minati & Paweł Oświȩcimka & Marek Stanuszek & Marcin Wa̧torek, 2019. "Signatures of the Crypto-Currency Market Decoupling from the Forex," Future Internet, MDPI, vol. 11(7), pages 1-18, July.
    2. Khraief, Naceur & Shahbaz, Muhammad & Mahalik, Mantu Kumar & Bhattacharya, Mita, 2021. "Movements of oil prices and exchange rates in China and India: New evidence from wavelet-based, non-linear, autoregressive distributed lag estimations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    3. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Pawe{l} O'swik{e}cimka & Tomasz Stanisz & Marcin Wk{a}torek, 2020. "Complexity in economic and social systems: cryptocurrency market at around COVID-19," Papers 2009.10030, arXiv.org.
    4. Kristjanpoller, Werner & Minutolo, Marcel C., 2021. "Asymmetric multi-fractal cross-correlations of the price of electricity in the US with crude oil and the natural gas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    5. Un, Kuok Sin & Ausloos, Marcel, 2022. "Equity premium prediction: Taking into account the role of long, even asymmetric, swings in stock market behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    6. Chai, Shanglei & Yang, Xiaoli & Zhang, Zhen & Abedin, Mohammad Zoynul & Lucey, Brian, 2022. "Regional imbalances of market efficiency in China’s pilot emission trading schemes (ETS): A multifractal perspective," Research in International Business and Finance, Elsevier, vol. 63(C).
    7. Naeem, Muhammad Abubakr & Hasan, Mudassar & Arif, Muhammad & Balli, Faruk & Shahzad, Syed Jawad Hussain, 2020. "Time and frequency domain quantile coherence of emerging stock markets with gold and oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    8. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Marcin Wk{a}torek, 2023. "What is mature and what is still emerging in the cryptocurrency market?," Papers 2305.05751, arXiv.org.
    9. Xu, Lin & Wu, Chenyang & Qin, Quande & Lin, Xiaoying, 2022. "Spillover effects and nonlinear correlations between carbon emissions and stock markets: An empirical analysis of China's carbon-intensive industries," Energy Economics, Elsevier, vol. 111(C).
    10. Mohamed Arbi Madani & Zied Ftiti, 2022. "Is gold a hedge or safe haven against oil and currency market movements? A revisit using multifractal approach," Annals of Operations Research, Springer, vol. 313(1), pages 367-400, June.
    11. Aslam, Faheem & Zil-e-huma, & Bibi, Rashida & Ferreira, Paulo, 2022. "Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis," Resources Policy, Elsevier, vol. 75(C).
    12. Stanis{l}aw Dro.zd.z & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek & Marcin Wk{a}torek, 2019. "Signatures of crypto-currency market decoupling from the Forex," Papers 1906.07834, arXiv.org, revised Jul 2019.
    13. Ghazani, Majid Mirzaee & Khosravi, Reza, 2020. "Multifractal detrended cross-correlation analysis on benchmark cryptocurrencies and crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    14. Rehman, Mobeen Ur & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Asymmetric multifractal behaviour and network connectedness between socially responsible stocks and international oil before and during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    15. Han, Mengjiao & Fan, Qingju & Ling, Guang, 2022. "Multiscale online-horizontal-visibility-graph correlation analysis of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    16. Alexander Musaev & Andrey Makshanov & Dmitry Grigoriev, 2022. "Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos," Mathematics, MDPI, vol. 10(4), pages 1-16, February.
    17. Choi, Sun-Yong, 2021. "Analysis of stock market efficiency during crisis periods in the US stock market: Differences between the global financial crisis and COVID-19 pandemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    18. Li, Shuping & Li, Jianfeng & Lu, Xinsheng & Sun, Yihong, 2022. "Exploring the dynamic nonlinear relationship between crude oil price and implied volatility indices: A new perspective from MMV-MFDFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    19. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2023. "Cryptocurrencies Are Becoming Part of the World Global Financial Market," Papers 2303.00495, arXiv.org.
    20. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    21. Espinosa-Paredes, G. & Rodriguez, E. & Alvarez-Ramirez, J., 2022. "A singular value decomposition entropy approach to assess the impact of Covid-19 on the informational efficiency of the WTI crude oil market," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

    More about this item

    Keywords

    Oil market; Forex; Multifractality; Detrended cross-correlations; Information flow;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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