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Giver and the receiver: Understanding spillover effects and predictive power in cross-market Bitcoin prices

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  • Gillaizeau, Marc
  • Jayasekera, Ranadeva
  • Maaitah, Ahmad
  • Mishra, Tapas
  • Parhi, Mamata
  • Volokitina, Evgeniia

Abstract

We identify and characterise the ‘givers and the receivers’ of volatility in cross-market Bitcoin prices and discuss international diversification strategies in this context. Using both time and frequency domain mechanisms, we provide estimates of outward and inward spillover effects. These have implications for (weak-form) cross-market inefficiency. In our setting, we treat high-degree of spillover as an indicator of weak-form inefficiency because investors can utilise information on the dynamic spillover effects to produce a best long-run prediction of the market. Our results show that Bitcoin prices depict strong (dynamic) spillover in volatility, especially during episodes of high uncertainty. The Bitcoin-USD exchange rate possesses net predictive power, mirrored by the tendency of the Bitcoin-EURO market as a net receiver relative to other markets. Robustness exercise generally supports our claim. The overall implication is that during episodes of high uncertainty, Bitcoin markets depict greater dynamic inefficiency, instrumenting the role of asymmetric information in the path-dependence and predictive power of Bitcoin prices in an interdependent market.

Suggested Citation

  • Gillaizeau, Marc & Jayasekera, Ranadeva & Maaitah, Ahmad & Mishra, Tapas & Parhi, Mamata & Volokitina, Evgeniia, 2019. "Giver and the receiver: Understanding spillover effects and predictive power in cross-market Bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 86-104.
    • Handle: RePEc:eee:finana:v:63:y:2019:i:c:p:86-104
    DOI: 10.1016/j.irfa.2019.03.005
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    Cited by:

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    2. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
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    4. Wang, Jinghua & Ngene, Geoffrey M., 2020. "Does Bitcoin still own the dominant power? An intraday analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    5. Arfaoui, Nadia & Naeem, Muhammad Abubakr & Boubaker, Sabri & Mirza, Nawazish & Karim, Sitara, 2023. "Interdependence of clean energy and green markets with cryptocurrencies," Energy Economics, Elsevier, vol. 120(C).
    6. Tobias Burggraf, 2020. "Bitcoin and Global Political Uncertainty – Evidence from the U.S. Election Cycle," Economics Bulletin, AccessEcon, vol. 40(1), pages 727-742.
    7. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    8. Tiantian Liu & Tadahiro Nakajima & Shigeyuki Hamori, 2022. "The impact of economic uncertainty caused by COVID-19 on renewable energy stocks," Empirical Economics, Springer, vol. 62(4), pages 1495-1515, April.
    9. Jiang, Wen & Xu, Qiuhua & Zhang, Ruige, 2022. "Tail-event driven network of cryptocurrencies and conventional assets," Finance Research Letters, Elsevier, vol. 46(PB).
    10. Huang, Yingying & Duan, Kun & Mishra, Tapas, 2021. "Is Bitcoin really more than a diversifier? A pre- and post-COVID-19 analysis," Finance Research Letters, Elsevier, vol. 43(C).
    11. Duan, Kun & Gao, Yang & Mishra, Tapas & Satchell, Stephen, 2023. "Efficiency dynamics across segmented Bitcoin Markets: Evidence from a decomposition strategy," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    12. Zhou, Mei-Jing & Huang, Jian-Bai & Chen, Jin-Yu, 2022. "Time and frequency spillovers between political risk and the stock returns of China's rare earths," Resources Policy, Elsevier, vol. 75(C).
    13. Stelios Bekiros & Axel Hedström & Evgeniia Jayasekera & Tapas Mishra & Gazi Salah Uddin, 2021. "Correlated at the Tail: Implications of Asymmetric Tail-Dependence Across Bitcoin Markets," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1289-1299, December.
    14. Luo, Di & Mishra, Tapas & Yarovaya, Larisa & Zhang, Zhuang, 2021. "Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    15. Zhang, Dingxuan & Sun, Yuying & Duan, Hongbo & Hong, Yongmiao & Wang, Shouyang, 2023. "Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin," International Review of Financial Analysis, Elsevier, vol. 88(C).
    16. Nikolaos A. Kyriazis, 2019. "A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets," JRFM, MDPI, vol. 12(4), pages 1-17, November.
    17. Costantini, Mauro & Maaitah, Ahmad & Mishra, Tapas & Sousa, Ricardo M., 2023. "Bitcoin market networks and cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    18. Fousekis, Panos & Tzaferi, Dimitra, 2021. "Returns and volume: Frequency connectedness in cryptocurrency markets," Economic Modelling, Elsevier, vol. 95(C), pages 13-20.
    19. Sofiane Aboura, 2022. "A note on the Bitcoin and Fed Funds rate," Empirical Economics, Springer, vol. 63(5), pages 2577-2603, November.
    20. Abubakr Naeem, Muhammad & Iqbal, Najaf & Lucey, Brian M. & Karim, Sitara, 2022. "Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    21. Proelss, Juliane & Sévigny, Stéphane & Schweizer, Denis, 2023. "GameFi: The perfect symbiosis of blockchain, tokens, DeFi, and NFTs?," International Review of Financial Analysis, Elsevier, vol. 90(C).
    22. Nikolaos A. Kyriazis, 2021. "Investigating the diversifying or hedging nexus of cannabis cryptocurrencies with major digital currencies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 845-861, December.

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    More about this item

    Keywords

    Cross-market Bitcoin prices; Return and volatility spillovers; Uncertainty; Inefficiency; Prediction;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • D5 - Microeconomics - - General Equilibrium and Disequilibrium

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