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Time-varying long range dependence in energy futures markets

Citations

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  1. Tiwari, Aviral Kumar & Umar, Zaghum & Alqahtani, Faisal, 2021. "Existence of long memory in crude oil and petroleum products: Generalised Hurst exponent approach," Research in International Business and Finance, Elsevier, vol. 57(C).
  2. Tiwari, Aviral Kumar & Kumar, Satish & Pathak, Rajesh & Roubaud, David, 2019. "Testing the oil price efficiency using various measures of long-range dependence," Energy Economics, Elsevier, vol. 84(C).
  3. Jiang, Yonghong & Nie, He & Ruan, Weihua, 2018. "Time-varying long-term memory in Bitcoin market," Finance Research Letters, Elsevier, vol. 25(C), pages 280-284.
  4. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Yoon, Seong-Min, 2018. "Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets," Finance Research Letters, Elsevier, vol. 27(C), pages 228-234.
  5. Guo, Yaoqi & Yao, Shanshan & Cheng, Hui & Zhu, Wensong, 2020. "China's copper futures market efficiency analysis: Based on nonlinear Granger causality and multifractal methods," Resources Policy, Elsevier, vol. 68(C).
  6. Agnello, Luca & Castro, Vítor & Hammoudeh, Shawkat & Sousa, Ricardo M., 2020. "Global factors, uncertainty, weather conditions and energy prices: On the drivers of the duration of commodity price cycle phases," Energy Economics, Elsevier, vol. 90(C).
  7. Yensen Ni, 2024. "Navigating Energy and Financial Markets: A Review of Technical Analysis Used and Further Investigation from Various Perspectives," Energies, MDPI, vol. 17(12), pages 1-22, June.
  8. Indranil SenGupta & William Nganje & Erik Hanson, 2021. "Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 39-55, March.
  9. Ding, Ashley, 2021. "A state-preference volatility index for the natural gas market," Energy Economics, Elsevier, vol. 104(C).
  10. Gök, Remzi & Hammoudeh, Shawkat & Ajmi, Ahdi Noomen, 2025. "Who’s more efficient and drives others? Profit sharing rates vs. deposit rates," The Quarterly Review of Economics and Finance, Elsevier, vol. 99(C).
  11. Corzo Santamaría, Teresa & Martin-Bujack, Karin & Portela, Jose & Sáenz-Diez, Rocio, 2022. "Early market efficiency testing among hydrogen players," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 723-742.
  12. Vogl, Markus, 2023. "Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framewo," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
  13. Humayra Shoshi & Indranil SenGupta, 2020. "Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model," Papers 2004.14862, arXiv.org, revised Feb 2021.
  14. Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
  15. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.
  16. Shimeng Shi & Jia Zhai & Yingying Wu, 2024. "Informational inefficiency on bitcoin futures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(6), pages 642-667, April.
  17. V Dimitrova & M Fernández-Martínez & M A Sánchez-Granero & J E Trinidad Segovia, 2019. "Some comments on Bitcoin market (in)efficiency," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-14, July.
  18. Ma, Pengcheng & Li, Daye & Li, Shuo, 2016. "Efficiency and cross-correlation in equity market during global financial crisis: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 163-176.
  19. Al-Yahyaee, Khamis Hamed & Rehman, Mobeen Ur & Mensi, Walid & Al-Jarrah, Idries Mohammad Wanas, 2019. "Can uncertainty indices predict Bitcoin prices? A revisited analysis using partial and multivariate wavelet approaches," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 47-56.
  20. Kristoufek, Ladislav, 2019. "Are the crude oil markets really becoming more efficient over time? Some new evidence," Energy Economics, Elsevier, vol. 82(C), pages 253-263.
  21. Nils Bundi & Marc Wildi, 2019. "Bitcoin and market-(in)efficiency: a systematic time series approach," Digital Finance, Springer, vol. 1(1), pages 47-65, November.
  22. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
  23. Charfeddine, Lanouar & Khediri, Karim Ben & Mrabet, Zouhair, 2019. "The forward premium anomaly in the energy futures markets: A time-varying approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 600-615.
  24. Li, Houjian & Luo, Fangyuan & Guo, Lili, 2024. "Harbor in the storm: How Bitcoin navigates challenges of climate change and global uncertainties," International Review of Economics & Finance, Elsevier, vol. 96(PB).
  25. Naeem, Muhammad Abubakr & Farid, Saqib & Yousaf, Imran & Kang, Sang Hoon, 2023. "Asymmetric efficiency in petroleum markets before and during COVID-19," Resources Policy, Elsevier, vol. 86(PA).
  26. Corbet, Shaen & Katsiampa, Paraskevi, 2020. "Asymmetric mean reversion of Bitcoin price returns," International Review of Financial Analysis, Elsevier, vol. 71(C).
  27. A. Sensoy & Benjamin M. Tabak, 2013. "How much random does European Union walk? A time-varying long memory analysis," Working Papers Series 342, Central Bank of Brazil, Research Department.
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