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Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series

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  • Obryan Poyser

Abstract

Currently, there is no consensus on the real properties of Bitcoin. The discussion comprises its use as a speculative or safe haven assets, while other authors argue that the augmented attractiveness could end accomplishing money's functions that economic theory demands. This paper explores the association between Bitcoin's market price and a set of internal and external factors using Bayesian Structural Time Series Approach. I aim to contribute to the discussion by differentiating among several attractiveness sources and employing a method that provides a more flexible analytic framework that decompose each of the components of the time series, apply variable selection, include information on previous studies, and dynamically examine the behavior of the explanatory variables, all in a transparent and tractable setting. The results show that the Bitcoin price is negatively associated with a neutral investor's sentiment, gold's price and Yuan to USD exchange rate, while positively related to stock market index, USD to Euro exchange rate and variated signs among the different countries' search trends. Hence, I find that Bitcoin has mixed properties since still seems to act as a speculative, safe haven and a potential a capital flights instrument.

Suggested Citation

  • Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.
    • Handle: RePEc:arx:papers:1706.01437
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    Cited by:

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    2. Yhlas Sovbetov, 2018. "Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 1-27.
    3. Zvonko Merkaš & Vlasta Roška, 2021. "The Impact of Unsystematic Factors on Bitcoin Value," JRFM, MDPI, vol. 14(11), pages 1-17, November.
    4. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
    5. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    6. Dag, Ali & Dag, Asli Z. & Asilkalkan, Abdullah & Simsek, Serhat & Delen, Dursun, 2023. "A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends," Journal of Business Research, Elsevier, vol. 156(C).
    7. Pavković Ana & Anđelinović Mihovil & Pavković Ivan, 2019. "Achieving Portfolio Diversification through Cryptocurrencies in European Markets," Business Systems Research, Sciendo, vol. 10(2), pages 85-107, September.
    8. Lin, Mei-Yin & An, Che-Lun, 2021. "The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach," Resources Policy, Elsevier, vol. 74(C).
    9. Mehmet Levent ERDAS & Abdullah Emre CAGLAR, 2018. "Analysis of the relationships between Bitcoin and exchange rate, commodities and global indexes by asymmetric causality test," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 9, pages 27-45, December.

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