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Cryptocurrencies under climate shocks: a dynamic network analysis of extreme risk spillovers

Author

Listed:
  • Kun Guo

    (University of Chinese Academy of Sciences
    Chinese Academy of Science)

  • Yuxin Kang

    (Chinese Academy of Science
    University of Chinese Academy of Sciences)

  • Qiang Ji

    (Chinese Academy of Sciences)

  • Dayong Zhang

    (Southwestern University of Finance and Economics)

Abstract

Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.

Suggested Citation

  • Kun Guo & Yuxin Kang & Qiang Ji & Dayong Zhang, 2024. "Cryptocurrencies under climate shocks: a dynamic network analysis of extreme risk spillovers," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-39, December.
  • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00579-y
    DOI: 10.1186/s40854-023-00579-y
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    as
    1. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Elsayed, Ahmed H. & Gozgor, Giray & Yarovaya, Larisa, 2022. "Volatility and return connectedness of cryptocurrency, gold, and uncertainty: Evidence from the cryptocurrency uncertainty indices," Finance Research Letters, Elsevier, vol. 47(PB).
    4. Sitara Karim & Muhammad Abubakr Naeem & Nawazish Mirza & Jessica Paule-Vianez, 2022. "Quantifying the hedge and safe-haven properties of bond markets for cryptocurrency indices," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 23(2), pages 191-205, January.
    5. Ji, Qiang & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2018. "Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 203-213.
    6. Sutap Kumar Ghosh & Md. Naiem Hossain & Hosneara Khatun, 2022. "The hedging role of US and Chinese stock markets against economic and trade policy uncertainty: lessons from recent turbulences," China Finance Review International, Emerald Group Publishing Limited, vol. 13(3), pages 444-470, December.
    7. Jiang, Yonghong & Lie, Jiayi & Wang, Jieru & Mu, Jinqi, 2021. "Revisiting the roles of cryptocurrencies in stock markets: A quantile coherency perspective," Economic Modelling, Elsevier, vol. 95(C), pages 21-34.
    8. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    9. Christophe Schinckus & Canh Phuc Nguyen & Felicia Chong Hui Ling, 2020. "Crypto-currencies Trading and Energy Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 355-364.
    10. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    11. Fry, John, 2018. "Booms, busts and heavy-tails: The story of Bitcoin and cryptocurrency markets?," Economics Letters, Elsevier, vol. 171(C), pages 225-229.
    12. Andrada-Félix, Julián & Fernandez-Perez, Adrian & Sosvilla-Rivero, Simón, 2020. "Distant or close cousins: Connectedness between cryptocurrencies and traditional currencies volatilities," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 67(C).
    13. Koutmos, Dimitrios, 2018. "Return and volatility spillovers among cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 122-127.
    14. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    15. Rodrigo Hakim das Neves, 2020. "Bitcoin pricing: impact of attractiveness variables," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-18, December.
    16. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    17. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    18. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    19. Fang, Tong & Su, Zhi & Yin, Libo, 2020. "Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility," International Review of Financial Analysis, Elsevier, vol. 71(C).
    20. Serda Selin Ozturk, 2020. "Dynamic Connectedness between Bitcoin, Gold, and Crude Oil Volatilities and Returns," JRFM, MDPI, vol. 13(11), pages 1-14, November.
    21. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    22. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    23. Shiyun Li & Yiping Huang, 2020. "Do Cryptocurrencies Increase the Systemic Risk of the Global Financial Market?," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(1), pages 122-143, January.
    24. Fernandes, Mário Correia & Dias, José Carlos & Nunes, João Pedro Vidal, 2021. "Modeling energy prices under energy transition: A novel stochastic-copula approach," Economic Modelling, Elsevier, vol. 105(C).
    25. Wang, Yizhi & Wei, Yu & Lucey, Brian M. & Su, Yang, 2023. "Return spillover analysis across central bank digital currency attention and cryptocurrency markets," Research in International Business and Finance, Elsevier, vol. 64(C).
    26. Bouri, Elie & Lau, Chi Keung Marco & Lucey, Brian & Roubaud, David, 2019. "Trading volume and the predictability of return and volatility in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 29(C), pages 340-346.
    27. David Y. Aharon & Zaghum Umar & Xuan Vinh Vo, 2021. "Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-25, December.
    28. Hsu, Shu-Han & Sheu, Chwen & Yoon, Jiho, 2021. "Risk spillovers between cryptocurrencies and traditional currencies and gold under different global economic conditions," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    29. Hong, Harrison & Li, Frank Weikai & Xu, Jiangmin, 2019. "Climate risks and market efficiency," Journal of Econometrics, Elsevier, vol. 208(1), pages 265-281.
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