Interest rates sensitivity arbitrage – theory and practical assesment for financial market trading

    Bohumil Stadnik   Affiliation


Purpose – Nowadays popular algorithmic trading uses many strategies which are algoritmizable and promise profitability. This research assess if it is possible successfully use interest rates sensitivity arbitrage in bond portfolio (also known as convexity arbitrage) in financial praxis. This arbitrage is sparsely described in literature and an assessment about its practical success is missing.

Research methodology – Methodology steps: mathematical definition of given arbitrage; construction of sufficient portfolio; backtesting on USD zero-coupon curves. Portfolio of two bonds is constructed (theoretically and practically) to have the same Macaulay duration and price, but a different convexity at certain YTM point. Therefore, being long the first bond while shorting the second (of higher convexity) would result in a market-directional bet for parallel zero-coupon yield curve shifts.

Findings – To construct practically the portfolio which is sufficient for the convexity arbitrage could be unrealistic on markets with low liquidity; the presumptions necessary to practically succeed are not fulfilled enough to ensure the arbitrage is profitable.

Research limitations – The backtesting is limited to USD market, testing other markets is recommended, but different result is not expected.

Practical implications – The research helps practitioners considering this strategy for its implementation to algorithmic trading.

Originality/Value – New important results for financial practitioners; states that practical and profitable utilization of convexity arbitrage is unrealizable and save costs during implementation of the strategy.

Keyword : convexity arbitrage, interest rate sensitivity, Macaulay duration, convexity, bond portfolio

How to Cite
Stadnik, B. (2021). Interest rates sensitivity arbitrage – theory and practical assesment for financial market trading. Business, Management and Economics Engineering, 19(1), 12-23.
Published in Issue
Feb 9, 2021
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Alexander, C., & Dimitriu, A. (2005). Indexing and statistical arbitrage. Journal of Portfolio Management, 31(2), 50–63.

Alsayed, H., & McGroarty, F. (2014). Ultra-high-frequency algorithmic arbitrage across international index futures. Journal of Forecasting, 33(6), 391–408.

Avellaneda, M., & Lee, J. H. (2008). Statistical arbitrage in the U.S. equities market.

Brandvold, M., Molnar, P., Vagstad, K., & Valstad, O. (2015). Price discovery on Bitcoin exchanges. Journal of International Financial Markets, Institutions and Money, 36, 18–35.

Birke, M., & Pilz, K. F. (2009). Nonparametric option pricing with no-arbitrage constraints. Journal of Financial Econometrics, 7(2), 53–76.

Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. The Review of Financial Studies, 27(8), 2267–2306.

Burgess, A. N. (2000). Statistical arbitrage models of the FTSE 100. In Y. S. Abu-Mostafa, B. LeBaron, A. W. Lo, & A. S. Weigend (Eds.), Computational finance 99 (pp. 297–312). MIT Press.

Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., & Vega, C. (2014). Rise of the machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69(5), 2045–2084.

Connor, G., & Lasarte, T. (2003). An overview of hedge fund strategies.

Cui, Z., Qian, W., Taylor, S., & Zhu, L. (2019). Detecting and identifying arbitrage in the spot foreign exchange market. Quantitative Finance, 20(1), 119–132.

Fama, E. (1969). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.

Focardi, S., Fabozzi, F., & Mitov, I. (2016). A new approach to statistical arbitrage: Strategies based on dynamic factor models of prices and their performance. Journal of Banking and Finance, 65, 134–155.

Gatarek, L., Hoogerheide, L., & van Dijk, H. K. (2014). Return and risk of pairs trading using a simulation-based Bayesian procedure for predicting stable ratios of stock prices (Discussion Papers 14-039/III). Tinbergen Institute.

Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs trading: Performance of a relativevalue arbitrage rule. The Review of Financial Studies, 19(3), 797–827.

Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? Journal of Finance, 66(1), 1–33.

Hillier, D., Draper, P., & Faff, R. (2006). Do precious metals shine? An investment perspective. Financial Analysts Journal, 62(2), 98–106.

Hogan, S., Jarrow, R., Theo, M., & Warachka, M. (2004). Testing market efficiency using statistical arbitrage with application to momentum and value strategies. Journal of Financial Economics, 73(3), 525–565.

Janda, K., Rausser, G., & Svarovska, B. (2014). Can investment in microfinance funds improve risk-return characteristics of a portfolio? Technological and Economic Development of Economy, 20(4), 673–695.

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: Traps in big data analysis. Science, 343(6176), 1203–1205.

Lintilhac, P., & Tourin, A. (2016). Model-based pairs trading in the bitcoin markets. Quantitative Finance, 17(5), 703–716.

Maeso, J., & Martellini, L. (2017). Factor investing and risk allocation: From traditional to alternative risk premia harvesting. The Journal of Alternative Investments, 20(1), 27–42.

Mahmoodzadeh, S., Tseng, M., & Gencay, R. (2019). Spot arbitrage in FX market and algorithmic trading: Speed is not of the essence.

McAfee, A., Brynjolfsson, E., Davenport, T., Patil, D., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90, 61–67.

Montana, G. (2009). Flexible least squares for temporal data mining and statistical arbitrage. Expert Systems with Applications, 36(2), 2819–2830.

Nardo, M., Petracco-Giudici, M., & Naltsidis, M. (2016). Walking down Wall Street with a tablet: A survey of stock market predictions using the Web. Journal of Economic Surveys, 30(2), 356–369.

Nath, P. (2006). High frequency pairs trading with us treasury securities: Risks and rewards for hedge funds (Working Paper Series). London Business School.

Nuti, G., Mirghaemi, M., Treleaven, P., & Yingsaeree, C. (2011). Algorithmic trading. Computer, 44(11), 61–69.

Ortega, L., & Khashanah, K. (2014). A neuro-wavelet model for the short-term forecasting of high-frequency time series of stock returns. Journal of Forecasting, 33(2), 134–146.

Payne, B., & Tresl, J. (2015). Hedge fund replication with a genetic algorithm: Breeding a usable mousetrap. Quantitative Finance, 15(10), 1705–1726.

Pole, A. (2007). Statistical arbitrage. John Wiley & Sons.

Questa, G. S. (1999). Fixed-income analysis for the global financial market: Money market, foreign exchanges, securities, and derivatives. John Wiley & Sons.

Ross, S. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360.

Saks, P., & Maringer, D. (2008). Genetic programming in statistical arbitrage. In M. Giacobini et al. (Eds.), Lecture notes in computer science: Vol. 4974. Applications of evolutionary computing. EvoWorkshops 2008 (pp. 73–82). Springer.

Shleifer, A., & Vishny, R. (1997). The limits of arbitrage. The Journal of Finance, 52, 35–55.

Stefanini, F. (2006). Investment strategies of Hedge Funds. John Wiley & Sons.

Thomaidis, N. S., Kondakis, N., & Dounias, G. D. (2006). An intelligent statistical arbitrage trading system. In G. Antoniou, G. Potamias, C. Spyropoulos, & D. Plexousakis (Eds.), Lecture notes in computer science: Vol. 3955. Advances in artificial intelligence. SETN 2006 (pp. 596–599). Springer.

Zapart, C. (2003, March 20–23). Statistical arbitrage trading with wavelets and artificial neural networks. In IEEE International Conference on Computational Intelligence for Financial Engineering (pp. 429–435). Hong Kong.