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Altcoin-Bitcoin Arbitrage

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  • Zura Kakushadze
  • Willie Yu

Abstract

We give an algorithm and source code for a cryptoasset statistical arbitrage alpha based on a mean-reversion effect driven by the leading momentum factor in cryptoasset returns discussed in https://ssrn.com/abstract=3245641. Using empirical data, we identify the cross-section of cryptoassets for which this altcoin-Bitcoin arbitrage alpha is significant and discuss it in the context of liquidity considerations as well as its implications for cryptoasset trading.

Suggested Citation

  • Zura Kakushadze & Willie Yu, 2019. "Altcoin-Bitcoin Arbitrage," Papers 1903.06033, arXiv.org, revised Apr 2019.
  • Handle: RePEc:arx:papers:1903.06033
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    References listed on IDEAS

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    1. Galina Hale & Arvind Krishnamurthy & Marianna Kudlyak & Patrick Shultz, 2018. "How Futures Trading Changed Bitcoin Prices," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    2. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    3. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-441, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Jamal Bouoiyour & Refk Selmi & Aviral Kumar Tiwari & Olaolu Richard Olayeni, 2016. "What drives Bitcoin price?," Economics Bulletin, AccessEcon, vol. 36(2), pages 843-850.
    5. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CIRJE F-Series CIRJE-F-1078, CIRJE, Faculty of Economics, University of Tokyo.
    6. 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.
    7. Young Bin Kim & Jun Gi Kim & Wook Kim & Jae Ho Im & Tae Hyeong Kim & Shin Jin Kang & Chang Hun Kim, 2016. "Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-17, August.
    8. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    9. Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-11, October.
    10. Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights," Papers 1503.06704, arXiv.org, revised Oct 2015.
    11. Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights," Post-Print hal-01277584, HAL.
    12. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin Technical Trading with Articial Neural Network," CIRJE F-Series CIRJE-F-1090, CIRJE, Faculty of Economics, University of Tokyo.
    13. Adrian (Wai-Kong) Cheung & Eduardo Roca & Jen-Je Su, 2015. "Crypto-currency bubbles: an application of the Phillips-Shi-Yu (2013) methodology on Mt. Gox bitcoin prices," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2348-2358, May.
    14. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-430, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    15. Tianyu Ray Li & Anup S. Chamrajnagar & Xander R. Fong & Nicholas R. Rizik & Feng Fu, 2018. "Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model," Papers 1805.00558, arXiv.org.
    16. Kakushadze, Zura, 2018. "Cryptoasset factor models," Algorithmic Finance, IOS Press, vol. 7(3-4), pages 87-104.
    17. Anne Haubo Dyhrberg, 2015. "Bitcoin, Gold and the Dollar – a GARCH Volatility Analysis," Working Papers 201520, School of Economics, University College Dublin.
    18. Sha Wang & Jean-Philippe Vergne, 2017. "Buzz Factor or Innovation Potential: What Explains Cryptocurrencies’ Returns?," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-17, January.
    19. Brandvold, Morten & Molnár, Peter & Vagstad, Kristian & Andreas Valstad, Ole Christian, 2015. "Price discovery on Bitcoin exchanges," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 36(C), pages 18-35.
    20. 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.
    21. Abeer ElBahrawy & Laura Alessandretti & Anne Kandler & Romualdo Pastor-Satorras & Andrea Baronchelli, 2017. "Evolutionary dynamics of the cryptocurrency market," Papers 1705.05334, arXiv.org, revised Nov 2017.
    22. Zura Kakushadze, 2014. "4-Factor Model for Overnight Returns," Papers 1410.5513, arXiv.org, revised Jun 2015.
    23. Gajardo, Gabriel & Kristjanpoller, Werner D. & Minutolo, Marcel, 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, Elsevier, vol. 109(C), pages 195-205.
    24. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    25. Nakano, Masafumi & Takahashi, Akihiko & Takahashi, Soichiro, 2018. "Bitcoin technical trading with artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 587-609.
    26. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
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