• S.I. : Financial Modelling and Risk Management of Energy and Environmental Instruments and Derivatives
  • Published:

Financial modelling, risk management of energy instruments and the role of cryptocurrencies

Abstract

This paper empirically investigates whether cryptocurrencies might have a useful role in financial modelling and risk management in the energy markets. To do so, the causal relationship between movements on the energy markets (specifically the price of crude oil) and the value of cryptocurrencies is analysed by drawing on daily data from April 2013 to April 2019. We find that shocks to the US and European crude oil indices are strongly connected to the movements of most cryptocurrencies. Applying a non-parametric statistic, Transferring Entropy (an econophysics technique measuring information flow), we find that some cryptocurrencies (XEM, DOGE, VTC, XLM, USDT, XRP) can be used for hedging and portfolio diversification. Furthermore, the results reveal that the European crude oil index is a source of shocks on the cryptocurrency market while the US oil index appears to be a receiver of shocks.

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Notes

  1. 1.

    https://www.ccn.com/cryptocurrency-accepted-venezuela-will-sell-oil-for-petro-maduro-says.

  2. 2.

    Petrol-coins refers to the kind of cryptocurrency which is called ‘Petrol’ backed by crude oil.

  3. 3.

    The investors are advised to minimize the variance by adding the opposite direction of correlation indices.

  4. 4.

    \( r_{\text{t}} = \ln \left( {\frac{{P_{\text{t}} }}{{P_{{{\text{t}} - 1}} }}} \right) \) in which Pt is the index at time t.

  5. 5.

    Many recent papers have applied this methodology for Bitcoin returns, but see in particular Jiang et al. (2018) and Sensoy (2019).

  6. 6.

    Please see more at https://www.torproject.org/.

  7. 7.

    The results are available upon request.

References

  1. Adler, G., & Sosa, S. (2011). Commodity price cycles: The perils of mismanaging the boom. Washington, DC: International Monetary Fund.

    Google Scholar 

  2. Adrangi, B., Chatrath, A., Raffiee, K., & Ripple, R. D. (2001). Alaska North Slope crude oil price and the behavior of diesel prices in California. Energy Economics,23(1), 29–42.

    Google Scholar 

  3. Aloui, R., Aïssa, M. S. B., & Nguyen, D. K. (2013). Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach. Journal of International Money and Finance,32, 719–738.

    Google Scholar 

  4. Ammous, S. (2018). The bitcoin standard: The decentralized alternative to central banking. Hoboken: Wiley.

    Google Scholar 

  5. Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. The review of economics and statistics,89(4), 701–720.

    Google Scholar 

  6. Asche, F., Gjølberg, O., & Völker, T. (2003). Price relationships in the petroleum market: An analysis of crude oil and refined product prices. Energy Economics,25(3), 289–301.

    Google Scholar 

  7. Atil, A., Lahiani, A., & Nguyen, D. K. (2014). Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices. Energy Policy,65, 567–573.

    Google Scholar 

  8. Bachmeier, L. J., & Griffin, J. M. (2006). Testing for market integration crude oil, coal, and natural gas. The Energy Journal,27, 55–71.

    Google Scholar 

  9. Baek, S. K., Jung, W. S., Kwon, O., & Moon, H. T. (2005). Transfer entropy analysis of the stock market. arXiv preprint physics/0509014.

  10. Balcilar, M., Bouri, E., Gupta, R., & Roubaud, D. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling,64, 74–81.

    Google Scholar 

  11. Balke, N. S., Brown, S. P., & Yücel, M. K. (2002). Oil price shocks and the US economy: Where does the asymmetry originate? The Energy Journal,23, 27–52.

    Google Scholar 

  12. Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters,161, 1–4.

    Google Scholar 

  13. Barnett, L., & Bossomaier, T. (2012). Transfer entropy as a log-likelihood ratio. Physical Review Letters,109(13), 138105.

    Google Scholar 

  14. Barsky, R. B., & Kilian, L. (2004). Oil and the Macroeconomy since the 1970s. Journal of Economic Perspectives,18(4), 115–134.

    Google Scholar 

  15. Baruník, J., & Kocenda, E. (2019). Total, asymmetric and frequency connectedness between oil and forex markets. The Energy Journal. https://doi.org/10.5547/01956574.40.SI2.jbar.

    Article  Google Scholar 

  16. Baur, Dirk G., Hong, Kihoon, & Lee, Adrian D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money,54, 177–189.

    Google Scholar 

  17. Beck, C., & Schlögl, F. (1993). Thermodynamics of chaotic systems. Cambridge: Cambridge Press.

    Google Scholar 

  18. Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics,183(2), 181–192.

    Google Scholar 

  19. Blau, B. M. (2017). Price dynamics and speculative trading in bitcoin. Research in International Business and Finance,41, 493–499.

    Google Scholar 

  20. Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives,29(2), 213–238.

    Google Scholar 

  21. Bollerslev, T., Patton, A. J., & Quaedvlieg, R. (2016). Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics,192(1), 1–18.

    Google Scholar 

  22. Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters,20, 192–198.

    Google Scholar 

  23. Brigida, M. (2014). The switching relationship between natural gas and crude oil prices. Energy Economics,43, 48–55.

    Google Scholar 

  24. Brown, S. P., & Yucel, M. K. (2008). What drives natural gas prices? Energy Journal,29(2), 45.

    Google Scholar 

  25. Chen, S. S., & Chen, H. C. (2007). Oil prices and real exchange rates. Energy Economics,29(3), 390–404.

    Google Scholar 

  26. Chen, K. C., Chen, S., & Wu, L. (2009). Price causal relations between China and the world oil markets. Global Finance Journal,20(2), 107–118.

    Google Scholar 

  27. Chuliá, H., Furió, D., & Uribe, J. M. (2019). Volatility spillovers in energy markets. The Energy Journal,40(3), 127–152.

    Google Scholar 

  28. Ciaian, P., & Rajcaniova, M. (2018). Virtual relationships: Short-and long-run evidence from BitCoin and altcoin markets. Journal of International Financial Markets, Institutions and Money,52, 173–195.

    Google Scholar 

  29. Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters,165, 28–34.

    Google Scholar 

  30. Corsi, F., Pirino, D., & Reno, R. (2010). Threshold bipower variation and the impact of jumps on volatility forecasting. Journal of Econometrics,159(2), 276–288.

    Google Scholar 

  31. Coudert, V., & Mignon, V. (2016). Reassessing the empirical relationship between the oil price and the dollar. Energy Policy,95, 147–157.

    Google Scholar 

  32. Degiannakis, S. (2008). ARFIMAX and ARFIMAX-TARCH realized volatility modeling. Journal of Applied Statistics,35(10), 1169–1180.

    Google Scholar 

  33. Degiannakis, S., & Filis, G. (2017). Forecasting oil price realized volatility using information channels from other asset classes. Journal of International Money and Finance,76, 28–49.

    Google Scholar 

  34. Dimpfl, T., & Peter, F. J. (2013). Using transfer entropy to measure information flows between financial markets. Studies in Nonlinear Dynamics and Econometrics,17(1), 85–102.

    Google Scholar 

  35. Duong, D., & Swanson, N. R. (2015). Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction. Journal of Econometrics,187(2), 606–621.

    Google Scholar 

  36. Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar—A GARCH volatility analysis. Finance Research Letters,16, 85–92.

    Google Scholar 

  37. Elder, J., & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit and Banking,42(6), 1137–1159.

    Google Scholar 

  38. Erdös, P., & Rényi, A. (1970). On a new law of large numbers. Journal d’analyse mathématique,23(1), 103–111.

    Google Scholar 

  39. Faes, L., Nollo, G., & Porta, A. (2013). Compensated transfer entropy as a tool for reliably estimating information transfer in physiological time series. Entropy,15(1), 198–219.

    Google Scholar 

  40. Furió, D., & Chuliá, H. (2012). Price and volatility dynamics between electricity and fuel costs: Some evidence for Spain. Energy Economics,34(6), 2058–2065.

    Google Scholar 

  41. Gajardo, G., Kristjanpoller, W. D., & Minutolo, M. (2018). Does Bitcoin exhibit the same asymmetric multifractal cross-correlations with crude oil, gold and DJIA as the Euro, Great British Pound and Yen? Chaos, Solitons & Fractals,109, 195–205.

    Google Scholar 

  42. German, H., El Karoui, N., & Rochet, J. C. (1995). Changes of numeraire, changes of probability measure and pricing of options. Journal of Applied Probability,32, 443–458.

    Google Scholar 

  43. Giudici, P., & Abu-Hashish, I. (2019). What determines bitcoin exchange prices? A network VAR approach. Finance Research Letters,28, 309–318.

    Google Scholar 

  44. Giudici, P., & Polinesi, G. (2019). Crypto price discovery through correlation networks. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03282-3.

    Article  Google Scholar 

  45. Gjolberg, O., & Johnsen, T. (1999). Risk management in the oil industry: Can information on long-run equilibrium prices be utilized? Energy Economics,21(6), 517–527.

    Google Scholar 

  46. Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M. (2014). Bitcoin-asset or currency? revealing users’ hidden intentions. Revealing Users’ Hidden Intentions (April 15, 2014). ECIS.

  47. Gong, X., & Lin, B. (2017). Forecasting the good and bad uncertainties of crude oil prices using a HAR framework. Energy Economics,67, 315–327.

    Google Scholar 

  48. Gong, X., & Lin, B. (2018a). Structural breaks and volatility forecasting in the copper futures market. Journal of Futures Markets,38(3), 290–339.

    Google Scholar 

  49. Gong, X., & Lin, B. (2018b). The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market. Energy Economics,74, 370–386.

    Google Scholar 

  50. Granger, C. W. (1988). Causality, cointegration, and control. Journal of Economic Dynamics and Control,12(2–3), 551–559.

    Google Scholar 

  51. Gronwald, M. (2019). Is Bitcoin a commodity? On price jumps, demand shocks, and certainty of supply. Journal of International Money and Finance,97, 86–92.

    Google Scholar 

  52. Guardian. (2013). NSA and GCHQ target Tor network that protects anonymity of web users. https://www.theguardian.com/world/2013/oct/04/nsa-gchq-attack-tor-network-encryption. Retrieved 23 April, 2020.

  53. Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2018). Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis, 63, 431–437.

    Google Scholar 

  54. Hamilton, J. D. (2003). What is an oil shock? Journal of Econometrics,113(2), 363–398.

    Google Scholar 

  55. Haugom, E., Langeland, H., Molnár, P., & Westgaard, S. (2014). Forecasting volatility of the US oil market. Journal of Banking & Finance,47, 1–14.

    Google Scholar 

  56. Hileman, G., & Rauchs, M. (2017). Global cryptocurrency benchmarking study. Cambridge Centre for Alternative Finance, 33, 33–113.

    Google Scholar 

  57. Hudson, R., & Urquhart, A. (2019). Technical trading and cryptocurrencies. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03357-1.

    Article  Google Scholar 

  58. Huynh, T. L. D. (2019). Spillover risks on cryptocurrency markets: A look from VAR-SVAR granger causality and student’st copulas. Journal of Risk and Financial Management,12(2), 1–19.

    Google Scholar 

  59. Huynh, T. L. D., Nguyen, S. P., & Duong, D. (2018). Contagion risk measured by return among cryptocurrencies. In International econometric conference of Vietnam (pp. 987–998). Springer, Cham.

  60. Jadidzadeh, A., & Serletis, A. (2017). How does the US natural gas market react to demand and supply shocks in the crude oil market? Energy Economics,63, 66–74.

    Google Scholar 

  61. Jiang, Y., Nie, H., & Ruan, W. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Letters,25, 280–284.

    Google Scholar 

  62. Jiao, J. L., Fan, Y., Wei, Y. M., Han, Z. Y., & Zhang, J. T. (2007). Analysis of the co-movement between Chinese and international crude oil price. International Journal of Global Energy Issues,27(1), 61–76.

    Google Scholar 

  63. Jin, X., Lin, S. X., & Tamvakis, M. (2012). Volatility transmission and volatility impulse response functions in crude oil markets. Energy Economics,34(6), 2125–2134.

    Google Scholar 

  64. Jin, J., Yu, J., Hu, Y., & Shang, Y. (2019). Which one is more informative in determining price movements of hedging assets? Evidence from Bitcoin, gold and crude oil markets. Physica A: Statistical Mechanics and its Applications,527, 121121.

    Google Scholar 

  65. Jizba, P., Kleinert, H., & Shefaat, M. (2012). Rényi’s information transfer between financial time series. Physica A: Statistical Mechanics and its Applications,391(10), 2971–2989.

    Google Scholar 

  66. Jo, S. (2014). The effects of oil price uncertainty on global real economic activity. Journal of Money, Credit and Banking,46(6), 1113–1135.

    Google Scholar 

  67. Kaiser, L. (2018). Seasonality in cryptocurrencies. Finance Research Letters. https://doi.org/10.1016/j.frl.2018.11.007.

    Article  Google Scholar 

  68. Kallinterakis, V. (2019). Do investors herd in cryptocurrencies—And why? Research in International Business and Finance. https://doi.org/10.1016/j.ribaf.2019.05.005.

    Article  Google Scholar 

  69. Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters,158, 3–6.

    Google Scholar 

  70. Keynes, J. M. (1923). Some aspects of commodity markets. Manchester Guardian Commercial: European Reconstruction Series,13, 784–786.

    Google Scholar 

  71. Kilian, L., & Park, C. (2009). The impact of oil price shocks on the US stock market. International Economic Review,50(4), 1267–1287.

    Google Scholar 

  72. Kim, J., Kim, G., An, S., Kwon, Y. K., & Yoon, S. (2013). Entropy-based analysis and bioinformatics-inspired integration of global economic information transfer. PLoS ONE,8(1), e51986.

    Google Scholar 

  73. Koutmos, D. (2018). Return and volatility spillovers among cryptocurrencies. Economics Letters,173, 122–127.

    Google Scholar 

  74. Koutmos, D. (2019). Market risk and Bitcoin returns. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03255-6.

    Article  Google Scholar 

  75. Krugman, P. (1983). Oil shocks and exchange rate dynamics. In Exchange rates and international macroeconomics (pp. 259–284). University of Chicago Press.

  76. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics,22(1), 79–86.

    Google Scholar 

  77. Kunkler, M., & MacDonald, R. (2019). The multilateral relationship between oil and G10 currencies. Energy Economics,78, 444–453.

    Google Scholar 

  78. Kwon, O., & Yang, J. S. (2008). Information flow between stock indices. EPL (Europhysics Letters),82(6), 68003.

    Google Scholar 

  79. Laherrere, J. (2006). Oil and gas: What future? World,1(292,549), 534.

    Google Scholar 

  80. Lanza, A., Manera, M., & Giovannini, M. (2005). Modeling and forecasting cointegrated relationships among heavy oil and product prices. Energy Economics,27(6), 831–848.

    Google Scholar 

  81. Li, J., Liang, C., Zhu, X., Sun, X., & Wu, D. (2013). Risk contagion in Chinese banking industry: A transfer entropy-based analysis. Entropy,15(12), 5549–5564.

    Google Scholar 

  82. Lin, S. X., & Tamvakis, M. N. (2001). Spillover effects in energy futures markets. Energy Economics,23(1), 43–56.

    Google Scholar 

  83. Liu, W. (2018). Portfolio diversification across cryptocurrencies. Finance Research Letters. https://doi.org/10.1016/j.frl.2018.07.010.

    Article  Google Scholar 

  84. Liu, T., & Gong, X. (2020). Analyzing time-varying volatility spillovers between the crude oil markets using a new method. Energy Economics,87, 104711.

    Google Scholar 

  85. Liu, L., Hu, H., Deng, Y., & Ding, N. (2014). An entropy measure of non-stationary processes. Entropy,16(3), 1493–1500.

    Google Scholar 

  86. Liu, Q., & Tu, A. H. (2012). Jump spillovers in energy futures markets: Implications for diversification benefits. Energy Economics,34(5), 1447–1464.

    Google Scholar 

  87. Liu, J., Wei, Y., Ma, F., & Wahab, M. I. M. (2017). Forecasting the realized range-based volatility using dynamic model averaging approach. Economic Modelling,61, 12–26.

    Google Scholar 

  88. Lizardo, R. A., & Mollick, A. V. (2010). Oil price fluctuations and US dollar exchange rates. Energy Economics,32(2), 399–408.

    Google Scholar 

  89. Lizier, J., & Mahoney, J. (2013). Moving frames of reference, relativity and invariance in transfer entropy and information dynamics. Entropy,15(1), 177–197.

    Google Scholar 

  90. Lizier, J. T., Prokopenko, M., & Zomaya, A. Y. (2008). Local information transfer as a spatiotemporal filter for complex systems. Physical Review E,77(2), 026110.

    Google Scholar 

  91. Ma, Y. R., Ji, Q., & Pan, J. (2019). Oil financialization and volatility forecast: Evidence from multidimensional predictors. Journal of Forecasting,38(6), 564–581.

    Google Scholar 

  92. Ma, F., Liu, J., Huang, D., & Chen, W. (2017). Forecasting the oil futures price volatility: A new approach. Economic Modelling,64, 560–566.

    Google Scholar 

  93. Marschinski, R., & Kantz, H. (2002). Analysing the information flow between financial time series. The European Physical Journal B-Condensed Matter and Complex Systems,30(2), 275–281.

    Google Scholar 

  94. Miller, M. H., & Scholes, M. (1972). Rates of return in relation to risk: A reexamination of some recent findings. Studies in the Theory of Capital Markets, 23, 47–48.

    Google Scholar 

  95. Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters,150, 6–9.

    Google Scholar 

  96. Nasir, M. A., Huynh, T. L. D., & Tram, H. T. X. (2019). Role of financial development, economic growth & foreign direct investment in driving climate change: A case of emerging ASEAN. Journal of Environmental Management,242, 131–141.

    Google Scholar 

  97. Peng, Y., Albuquerque, P. H. M., de Sá, J. M. C., Padula, A. J. A., & Montenegro, M. R. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. Expert Systems with Applications,97, 177–192.

    Google Scholar 

  98. Peter, F. J., Dimpfl, T., & Huergo, L. (2011). Using transfer entropy to measure information flows from and to the CDS market. In Midwest Finance Association 2012 annual meetings paper. http://ssrn.com/abstract (Vol. 1683948). Accessed 30 Sept 2019.

  99. Phan, D. H. B., Sharma, S. S., & Narayan, P. K. (2016). Intraday volatility interaction between the crude oil and equity markets. Journal of International Financial Markets, Institutions and Money,40, 1–13.

    Google Scholar 

  100. Pieters, G., & Vivanco, S. (2017). Financial regulations and price inconsistencies across Bitcoin markets. Information Economics and Policy,39, 1–14.

    Google Scholar 

  101. Pindyck, R. S. (2003). Volatility in natural gas and oil markets. Journal of Energy and Development,30(1), 1–19.

    Google Scholar 

  102. Prokopczuk, M., Symeonidis, L., & Wese Simen, C. (2016). Do jumps matter for volatility forecasting? Evidence from energy markets. Journal of Futures Markets,36(8), 758–792.

    Google Scholar 

  103. Prokopenko, M., Lizier, J., & Price, D. (2013). On thermodynamic interpretation of transfer entropy. Entropy,15(2), 524–543.

    Google Scholar 

  104. Ramberg, D. J., & Parsons, J. E. (2010). The weak tie between natural gas and oil prices. Center for Energy and Environmental Policy Research (CEEPR) No, 10-017.

  105. Raymaekers, W. (2015). Cryptocurrency Bitcoin: Disruption, challenges and opportunities. Journal of Payments Strategy & Systems,9(1), 30–46.

    Google Scholar 

  106. Reboredo, J. C. (2012). Modelling oil price and exchange rate co-movements. Journal of Policy Modeling,34(3), 419–440.

    Google Scholar 

  107. Reboredo, J. C., Rivera-Castro, M. A., & Zebende, G. F. (2014). Oil and US dollar exchange rate dependence: A detrended cross-correlation approach. Energy Economics,42, 132–139.

    Google Scholar 

  108. Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics,34(1), 248–255.

    Google Scholar 

  109. Schreiber, T. (2000). Measuring information transfer. Physical Review Letters,85(2), 461.

    Google Scholar 

  110. Selmi, R., Mensi, W., Hammoudeh, S., & Bouoiyour, J. (2018). Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold. Energy Economics,74, 787–801.

    Google Scholar 

  111. Sensoy, A. (2019). The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters,28, 68–73.

    Google Scholar 

  112. Serletis, A. (1994). A cointegration analysis of petroleum futures prices. Energy Economics,16(2), 93–97.

    Google Scholar 

  113. Serletis, A., & Rangel-Ruiz, R. (2004). Testing for common features in North American energy markets. Energy Economics,26(3), 401–414.

    Google Scholar 

  114. Sévi, B. (2014). Forecasting the volatility of crude oil futures using intraday data. European Journal of Operational Research,235(3), 643–659.

    Google Scholar 

  115. Shahbaz, M., Nasir, M. A., & Roubaud, D. (2018). Environmental degradation in France: The effects of FDI, financial development, and energy innovations. Energy Economics,74, 843–857.

    Google Scholar 

  116. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal,27(3), 379–423.

    Google Scholar 

  117. Stokes, R. (2012). Virtual money laundering: the case of Bitcoin and the Linden dollar. Information & Communications Technology Law,21(3), 221–236.

    Google Scholar 

  118. Sumioka, H., Yoshikawa, Y., & Asada, M. (2007). Causality detected by transfer entropy leads acquisition of joint attention. In 2007 IEEE 6th international conference on development and learning (pp. 264–269). IEEE.

  119. Symitsi, E., & Chalvatzis, K. J. (2018). Return, volatility and shock spillovers of Bitcoin with energy and technology companies. Economics Letters,170, 127–130.

    Google Scholar 

  120. Symitsi, E., & Chalvatzis, K. J. (2019). The economic value of Bitcoin: A portfolio analysis of currencies, gold, oil and stocks. Research in International Business and Finance,48, 97–110.

    Google Scholar 

  121. Tian, F., Yang, K., & Chen, L. (2017). Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity. International Journal of Forecasting,33(1), 132–152.

    Google Scholar 

  122. Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters,148, 80–82.

    Google Scholar 

  123. Van Wijk, D. (2013). What can be expected from the BitCoin. Rotterdam: Erasmus Universiteit Rotterdam.

    Google Scholar 

  124. Vandezande, N. (2017). Virtual currencies under EU anti-money laundering law. Computer Law & Security Review,33(3), 341–353.

    Google Scholar 

  125. Ver Steeg, G., & Galstyan, A. (2012). Information transfer in social media. In Proceedings of the 21st international conference on World Wide Web (pp. 509–518). ACM.

  126. Wang, Y., Ma, F., Wei, Y., & Wu, C. (2016a). Forecasting realized volatility in a changing world: A dynamic model averaging approach. Journal of Banking & Finance,64, 136–149.

    Google Scholar 

  127. Wang, J., Xue, Y., & Liu, M. (2016a). An analysis of bitcoin price based on VEC model. In 2016 international conference on economics and management innovations. Atlantis Press.

  128. Wen, F., Gong, X., & Cai, S. (2016). Forecasting the volatility of crude oil futures using HAR-type models with structural breaks. Energy Economics,59, 400–413.

    Google Scholar 

  129. White, R., Marinakis, Y., Islam, N., & Walsh, S. (2020). Is Bitcoin a currency, a technology-based product, or something else? Technological Forecasting and Social Change,151, 119877.

    Google Scholar 

  130. Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In D. Lee Kuo Chuen and S. Kee Boon (Eds), Handbook of digital currency (pp. 31–43). Academic Press.

  131. Zamani, N. (2016). How the crude oil market affects the natural gas market? Demand and supply shocks. International Journal of Energy Economics and Policy,6(2), 217–221.

    Google Scholar 

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Appendix robustness check

Appendix robustness check

WTI crude oil robustness check

Causality Full sample Frequency-connectedness
DCOILWTICO → LTC 0.0117*
[0.0028]
0.74
DCOILWTICO → ETH 0.0268***
[0.0043]
0.01
DCOILWTICO → DASH 0.0171**
[0.0032]
1.15
DASH → DCOILWTICO 0.0140*
[0.0033]
0.01
DCOILWTICO → XMR 0.0136*
[0.0032]
0.66
DCOILWTICO → XVG 0.0132*
[0.0033]
13.83
XVG → DCOILWTICO 0.0184**
[0.0035]
7.50
DCOILWTICO → MAID 0.0107*
[0.0031]
2.68

See Fig. 7.

Fig. 7
figure7

The connectedness between cryptocurrency and crude oil markets

BRENT crude oil

Causality Renyi transfer entropy Frequency-connectedness
DCOILBRENTEU → BTC 0.0113*
[0.0025]
0.01
BTC → DCOILBRENTEU 0.0106*
[0.0026]
0.11
DCOILBRENTEU → LTC 0.0116*
[0.0028]
0.09
DCOILBRENTEU → ETH 0.0177**
[0.0041]
0.16
DCOILBRENTEU → XMR 0.0128*
[0.0032]
0.11
DGB → DCOILBRENTEU 0.0152**
[0.0031]
0.02
DCOILBRENTEU → MAID 0.0120*
[0.0032]
0.02

Information on the variable’s full name

This appendix provides the insights of each cryptocurrency in terms of name, market capitalization, total supply and all time high in the exchange.

Symbol Name Market capitalization (USD) Circulating supply (unit) Date of issue
BTC Bitcoin 174,918,379,274 18,236,475 01st May 2013
LTC Litecoin 4,769,521,120 64,159,150 01st May 2013
ETH Ethereum 28,589,119,108 109,844,644 8th Aug 2015
XEM NEM 21,240,638 8,999,999,999 3rd Apr 2015
DASH Dash 938,226,050 9,344,247 15th Feb 2014
DOGE Dogecoin 315,532,005 123,461,542,248 19th Dec 2013
XMR Monero 1,398,868,850 17,458,076 21st May 2014
VTC Vertcoin 16,413,384 53,593,747 20th Jan 2014
XVG Verge 69,366,673 16,187,838,743 26th Oct 2014
DGB DigiByte 84,789,677 12,859,912,607 07th Feb 2014
XLM Stellar 1,370,120,659 20,205,010,713 05th Aug 2014
USDT Tether 4,653,741,622 4,642,367,414 07th March 2015
MAID MaidSafeCoin 43,986,645 452,552,412 28th Apr 2014
XRP Ripple 11,642,567,056 43,749,413,421 07th Aug 2013
  1. Our database is updated to 25th February 2020 and retreived from coinmarketcap.com

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Huynh, T.L.D., Shahbaz, M., Nasir, M.A. et al. Financial modelling, risk management of energy instruments and the role of cryptocurrencies. Ann Oper Res (2020). https://doi.org/10.1007/s10479-020-03680-y

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Keywords

  • Energy markets
  • Risk management
  • Crude oil
  • Cryptocurrency
  • Transfer entropy
  • Financial instruments

JEL Classification

  • O31
  • G18
  • O32
  • O33