Pairs trading in cryptocurrency market: A long-short story
-
DOIhttp://dx.doi.org/10.21511/imfi.18(3).2021.12
-
Article InfoVolume 18 2021, Issue #3, pp. 127-141
- Cited by
- 1102 Views
-
5371 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Pairs trading that is built on ’Relative-Value Arbitrage Rule’ is a popular short-term speculation strategy enabling traders to make profits from temporary mispricing of close substitutes. This paper aims at investigating the profit potentials of pairs trading in a new finance area – on cryptocurrencies market. The empirical design builds upon four well-known approaches to implement pairs trading, namely: correlation analysis, distance approach, stochastic return differential approach, and cointegration analysis, that use monthly closing prices of leading cryptocoins over the period January 1, 2018, – December 31, 2019. Additionally, the paper executes a simulation exercise that compares long-short strategy with long-only portfolio strategy in terms of payoffs and risks. The study finds an inverse relationship between the correlation coefficient and distance between different pairs of cryptocurrencies, which is a prerequisite to determine the potentially market-neutral profits through pairs trading. In addition, pairs trading simulations produce quite substantive evidence on the continuing profitability of pairs trading. In other words, long-short portfolio strategies, producing positive cumulative returns in most subsample periods, consistently outperform conservative long-only portfolio strategies in the cryptocurrency market. The profitability of pairs trading thus adds empirical challenge to the market efficiency of the cryptocurrency market. However, other aspects like spectral correlations and implied volatility might also be significant in determining the profit potentials of pairs trading.
- Keywords
-
JEL Classification (Paper profile tab)C58, D53, G12
-
References53
-
Tables6
-
Figures4
-
- Figure 1. Cointegration residuals in Panel A
- Figure 2. Cointegration residuals in Panel B
- Figure 3. Cointegration residuals in Panel C
- Figure 4. Cointegration residuals in Panel D
-
- Table 1. Summary statistics of cryptocurrencies
- Table 2. Correlation and distance
- Table 3. Augmented Dickey-Fuller (ADF) tests on cryptocurrencies prices
- Table 4. Engle-Granger cointegration analysis of cryptocurrencies
- Table 5. Augmented Dickey-Fuller (ADF) tests on residual series
- Table 6. Profit payoffs from pairs trading in simulated exercises
-
- Ahmed, S., Grobys, K., & Sapkota, N. (2020). Profitability of technical trading rules among cryptocurrencies with privacy function. Financial Research Letters, 35, 101495.
- Al-Yahyaee, K. H., Mensi, W., & Yoon, S-M. (2018). Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228-234.
- Andrianto, Y., & Diputra, Y. (2017). The effect of cryptocurrency on investment portfolio effectiveness. Journal of Finance and Accounting, 5(6), 229-238.
- Asteriou, D., & Hall, S. G. (2011). Applied Econometrics. New York: Palgrave Macmillan.
- Baur, D. G., Hong, K. I., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money, 54, 177-189.
- Bernoulli, D. (1954). Exposition of a new theory on the measurement of risk. Econometrica, 22(1), 23-36.
- Blazquez, M. C., De la Cruz, C. O., & Roman, C. P. (2018). Pairs trading techniques: An empirical contrast. European Research on Management and Business Economics, 24(3), 160-167.
- Bohme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-238.
- Bouri, E., Molnar, P., Azzi, G., Roubaud, D., & Hagfors, L.I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198.
- Brooks, C. (2008). Introductory Econometrics for Finance. New York, NY: Cambridge University Press.
- Cao, C., Simin, T., & Wang, Y. (2013). Do mutual fund managers time market liquidity? Journal of Financial Markets, 16(2), 279-307.
- Chiu, M. C., & Wong, Y. (2015). Dynamic cointegrated pairs trading: time-consistent mean-variance strategies. Journal of Computational and Applied Mathematics, 290, 516-534.
- Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28-34.
- Dirican, C., & Canoz, I. (2017). The cointegration relationship between Bitcoin prices and major world stock indices: an analysis with ARDL model approach. Journal of Economics, Finance and Accounting, 4(4), 377-392.
- Do, B., & Faff, R. (2010). Does simple pairs trading still work? Financial Analysts Journal, 66(4), 83-95.
- Do, B., Faff, R., & Hamza, K. (2006). A new technique to modeling and estimation for pairs trading. Proceedings of 2006 Financial Management Association European Conference, 87-99
- Ehrman, D. (2006). The handbook of pairs trading: Strategies using equities, options, and futures. New Jersey: John Wiley & Sons.
- Engle, R., & Granger, C. (1987). Cointegration and error correction. Representation, estimation and testing. Econometrica, 55(2), 251-276.
- Fabozzi, F. J., Stoyanov, S. V., & Rachev, S. T. (2013). Computational aspects of portfolio risk estimation in volatile markets: a survey. Studies in Nonlinear Dynamics & Econometrics, 17(1), 103-120.
- Fil, M., & Kristoufek, L. (2020). Pairs trading in cryptocurrency markets. IEEE Access, 8, 172644-172651.
- Foley, S., Karlsen, J., & Putnins, T. (2019). Sex, drugs, and Bitcoin: How much illegal activity is financed through cryptocurrencies? Review of Financial Studies, 32(5), 1798-1853.
- Gatev, E. G., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs trading: performance of a relative value arbitrage rule. Review of Financial Studies, 19(3), 797-827.
- Giudici, G., Milne, A., & Vinogradov, D. (2020). Cryptocurrencies: market analysis and perspectives. Journal of Industrial Business and Economics, 47, 1-18.
- Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M. (2014). Bitcoin – asset or currency? Revealing users’ hidden intentions. In: Twenty Second European Conference on Information Systems, 1-14.
- Granger, C. W. J., & Newbold, P. (1986). Forecasting Economic Time Series. San Diego, CA: Academic Press.
- Gujarati, D. N., Porter, D. C., & Gunasekar, S. (2011). Basic Econometrics. New Delhi: Tata McGraw Hill Publications.
- Haryanto, S. Subroto, A. & Ulpah, M. (2020). Disposition effect and herding behavior in the cryptocurrency market. Journal of Industrial and Business Economics, 47(1), 115-132.
- Hull, J. C. (2005). Options, Futures and Other Derivatives. New York: Prentice Hall Finance series.
- Joyce, J. M., & Vogel, R. C. (1970). The uncertainty in risk: is variance unambiguous? The Journal of Finance, 25(1), 127-134.
- Jurek, J. W., & Yang, H. (2007). Dynamic portfolio selection in arbitrage (Working paper). Harvard University.
- Krokhmal, P., Uryasev, S., & Palmquist, J. (2001). Portfolio optimization with conditional value-at-risk objective and constraints. Journal of Risk, 4(2), 43-68.
- Kumar, S. A., & Ajaz, T. (2019). Co-movement in crypto-currency markets: evidences from wavelet analysis. Financial Innovation, 5, 33.
- Kupiec, P. H., & Sharpe, S. A (1991). Animal spirits, margin requirements, and stock price volatility. Journal of Finance, 46(2), 717-731.
- Lee, D. K. C., Guo, L., & Wang, Y. (2018). Cryptocurrency: A new investment opportunity? Journal of Alternative Investments, 20(3), 16-40.
- Leung, T., & Nguyen, H. (2019). Constructing cointegrated cryptocurrency portfolios for statistical arbitrage. Studies in Economics and Finance, 36(3), 581-599.
- Liew, R., & Wu, Y. (2013). Pairs trading: A copula approach. Journal of Derivatives and Hedge Funds, 19, 12-30.
- Lintilhac, P. S., & Tourin, A. (2017). Model-based pairs trading in the bitcoin markets. Quantitative Finance, 17(5), 703-716.
- Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
- Markowitz, H. M. (1999). The early history of portfolio theory: 1600–1960. Financial Analyst Journal, 55(4), 5-16.
- Meissner, G. (2016). Correlation trading strategies – opportunities and limitations. Journal of Trading, 11(4), 14-32.
- Nath, P. (2003). High frequency Pairs trading with U.S. treasury securities: risks and rewards for hedge funds (Working Paper). London Business School.
- Petukhina, A., Trimborn, S., Härdle, W. K., & Elendner, H. (2020). Investing with Cryptocurrencies – evaluating their potential for portfolio allocation strategies. SSRN Electronic Journal.
- Ramos-Requena, J. P., Trinidad-Segovia, J. E., & Sánchez-Granero, M. A. (2020). Some notes on the formation of a pair in Pairs trading. Mathematics, 8(3), 348.
- Rubenstein, M. (2002). Markowitz’s “Portfolio Selection”: A fifty-year retrospective. Journal of Finance, 57(3), 1041-4045.
- Saji, T. G. (2014). Sector effects in emerging market returns: evidence from India. Artha Vijnana, 55(3), 402-417.
- Saksonova, S., & Kuzmina-Merlino, I. (2019) Cryptocurrency as an Investment Instrument in a Modern Financial Market. St Petersburg University Journal of Economic Studies, 35(2), 269-282.
- Smith, C., & Kumar, A. (2018) Crypto-currencies – an introduction to not-so-funny moneys. Journal of Economic Surveys, 32(5), 1531-1559.
- Smith, R. T., & Xu, X. (2017). A good pair: alternative pairs-trading strategies. Financial Markets and Portfolio Management, 31(1), 1-26.
- Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
- Van den Broek, L., & Sharif, Z. (2018). Cointegration-based pairs trading framework with application to the cryptocurrency market (Bachelor Thesis). Erasmus University Rotterdam.
- Vidyamurthy, G. (2004). Pairs Trading: Quantitative Methods and Analysis. John Wiley & Sons.
- Wang, J. (2009). A high performance pair trading application. IEEE International Symposium on Parallel and Distributed Processing. Rome.
- Whistler, M. (2004). Trading pairs: capturing profits and hedging risk with statistical arbitrage strategies. John Wiley & Sons.