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Closing a Bitcoin Trade Optimally under Partial Information: Performance Assessment of a Stochastic Disorder Model

Author

Listed:
  • Zehra Eksi

    (Institute for Statistics and Mathematics, WU-University of Economics and Business, Welthandelsplatz 1, 1020 Vienna, Austria)

  • Daniel Schreitl

    (Institute for Statistics and Mathematics, WU-University of Economics and Business, Welthandelsplatz 1, 1020 Vienna, Austria)

Abstract

The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade.

Suggested Citation

  • Zehra Eksi & Daniel Schreitl, 2022. "Closing a Bitcoin Trade Optimally under Partial Information: Performance Assessment of a Stochastic Disorder Model," Mathematics, MDPI, vol. 10(1), pages 1-13, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:1:p:157-:d:717970
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    References listed on IDEAS

    as
    1. Bariviera, Aurelio F. & Basgall, María José & Hasperué, Waldo & Naiouf, Marcelo, 2017. "Some stylized facts of the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 82-90.
    2. repec:bla:jfinan:v:53:y:1998:i:6:p:1839-1885 is not listed on IDEAS
    3. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    4. Tzouvanas, Panagiotis & Kizys, Renatas & Tsend-Ayush, Bayasgalan, 2020. "Momentum trading in cryptocurrencies: Short-term returns and diversification benefits," Economics Letters, Elsevier, vol. 191(C).
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    Cited by:

    1. Andrew Phiri, 2022. "Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 373-386, September.

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