Convexity arbitrage – the idea which does not work

References

• Alexander, C., & Dimitriu, A. (2005). Indexing and statistical arbitrage. Journal of Portfolio Management, 31(2), 50–16. https://doi.org/10.3905/jpm.2005.470578
   Web of Science ®Google Scholar
• Alsayed, H., & McGroarty, F. (2014). Ultra-High-Frequency algorithmic arbitrage across international index futures. Journal of Forecasting, 33(6), 391–408. https://doi.org/10.1002/for.2298
   Web of Science ®Google Scholar
• Avellaneda, M., & Lee, J. (2008). Statistical arbitrage in the U.S. equities market. Political Methods: Quantitative Methods eJournal. https://www.researchgate.net/profile/Marco-Avellaneda/publication/265264751_Statistical_Arbitrage_in_the_US_Equity_Market/links/54e732120cf277664ff7ff67/Statistical-Arbitrage-in-the-US-Equity-Market.pdf
   Google Scholar
• Birke, M., & Pilz, K. F. (2009). Nonparametric option pricing with no-arbitrage constraints. Journal of Financial Econometrics, 7(2), 53–76. https://doi.org/10.1093/jjfinec/nbn016
   Web of Science ®Google Scholar
• Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. The Review of Financial Studies, 27(8), 2267–2306. https://doi.org/10.1093/rfs/hhu032
   Web of Science ®Google Scholar
• Burgess, A. N. (2000). Statistical arbitrage models of the FTSE 100. In Abu-Mostafa Y., LeBaron, B., Lo, A. W., &Weigend, A. S. (Eds.), Computational Finance 1999, (pp. 297–312). MIT Press.
   Google Scholar
• 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. https://doi.org/10.1111/jofi.12186
   Web of Science ®Google Scholar
• Connor, G., & Lasarte, T. (2003). An overview of hedge fund strategies. Working Paper. https://www.researchgate.net/profile/Gregory-Connor/publication/251337307_An_Overview_of_Hedge_Fund_Strategies/links/543be4440cf204cab1db4667/An-Overview-of-Hedge-Fund-Strategies.pdf
   Google Scholar
• Cui, Z., Qian, W., Taylor, S., & Zhu, L. (2020). Detecting and identifying arbitrage in the spot foreign exchange market. Quantitative Finance 20(1), 119–132. https://doi.org/10.1080/14697688.2019.1639801
   Web of Science ®Google Scholar
• Fabozzi, F. J. (2010). Bond Markets, Analyses and Strategies. (chapter 5: Factors Affecting Bond Yields and the Term Structure of Interest Rates. seventh). Pearson.
   Google Scholar
• Fama, E. (1969). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.jbankfin.2015.10.005
   Web of Science ®Google Scholar
• 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. Tinbergen Institute Discussion Papers 14-039/III, Tinbergen Institute.
   Google Scholar
• Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. https://doi.org/10.1093/rfs/hhj020
   Web of Science ®Google Scholar
• Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), 1–33. https://doi.org/10.1111/j.1540-6261.2010.01624.x
   Web of Science ®Google Scholar
• Hillier, D., Draper, P., & Faff, R. (2006). Do precious metals shine? An investment perspective. Financial Analysts Journal, 62(2), 98–106. https://doi.org/10.2469/faj.v62.n2.4085
   Web of Science ®Google Scholar
• 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, 525–565. https://doi.org/10.1016/j.jfineco.2003.10.004
   Web of Science ®Google Scholar
• Janda, K., & Kourilek, J. (2020). Residual shape risk on natural gas market with mixed jump diffusion price dynamics. Energy Economics, 85(104465).
   Google Scholar
• Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of google flu: Traps in big data analysis. Science, 343(6176), 1203–1205. https://doi.org/10.1126/science.1248506
   PubMed Web of Science ®Google Scholar
• Lintilhac, P., & Tourin, A. (2017). Model-based pairs trading in the bitcoin markets. Quantitative Finance 17(5) , 703–716. https://doi.org/10.1080/14697688.2016.1231928
   Web of Science ®Google Scholar
• 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. https://doi.org/10.3905/jai.2017.20.1.027
   Web of Science ®Google Scholar
• Mahmoodzadeh, S., Tseng, M., & Gencay, R. (2019). Spot Arbitrage in FX market and algorithmic trading: Speed is not of the essence (Retrived March 25, 2019). SSRN from https://ssrn.com/abstract=3039407
   Google Scholar
• McAfee, A., Brynjolfsson, E., Davenport, T., Patil, D., & Barton, D. (2012). Big data. the management revolution. Harvard Business Review, 90, 61–67.
   Web of Science ®Google Scholar
• Montana, G. (2009). Flexible least squares for temporal data mining and statistical arbitrage. Expert Systems with Applications, 36(2), 2819–2830. https://doi.org/10.1016/j.eswa.2008.01.062
   Web of Science ®Google Scholar
• Nardo, M., Petracco, 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. https://doi.org/10.1111/joes.12102
   Web of Science ®Google Scholar
• Nath, P. (2006). High frequency pairs trading with US treasury securities: Risks and rewards for hedge funds. Working Paper Series. London Business School
   Google Scholar
• Nuti, G., Mirghaemi, M., Treleaven, P., & Yingsaeree, C. (2011). Algorithmic trading. Computer, 44(11), 61–69. https://doi.org/10.1109/MC.2011.31
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1002/for.2270
   Web of Science ®Google Scholar
• Ortobelli, S., Cassader, M., Vitali, S., & Tichý, T. (2018). Portfolio selection strategy for the fixed income markets with immunization on average. Annals of Operations Research, 260(1–2), 395–415. https://doi.org/10.1007/s10479-016-2182-8
   Web of Science ®Google Scholar
• Payne, B., & Tresl, J. (2015). Hedge fund replication with a genetic algorithm: Breeding a usable mousetrap. Quantitative Finance, 15(10), 1705–1726. https://doi.org/10.1080/14697688.2014.979222
   Web of Science ®Google Scholar
• Pole, A. (2007). Statistical Arbitrage. John Wiley аnd Sons.
   Google Scholar
• Questa, G. S. (1999). .Fixed-income analysis for the global financial market: Money market, foreign exchanges, securities, and derivatives. (Vol. 10). Kenneth R. French, NTU Professor of Finance Sloan School of Management, MIT: John Wiley аnd Sons.
   Google Scholar
• Ross, S. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360. https://doi.org/10.1016/0022-0531(76)90046-6
   Web of Science ®Google Scholar
• Saks, P., & Maringer, D. (2009). Statistical arbitrage with genetic programming. In A. Brabazon, and M. O’Neill (Eds.), Natural computing in computational finance. Studies in computational intelligence (Vol. 185). Berlin: Springer. https://doi.org/10.1007/978-3-540-95974-8_2
   Google Scholar
• Shleifer, A., & Vishny, R. (1997). The limits of arbitrage. The Journal of Finance, 52(1), 35–55. https://doi.org/10.1111/j.1540-6261.1997.tb03807.x
   Web of Science ®Google Scholar
• Stefanini, F. (2006). Investment strategies of hedge funds. John Wiley аnd Sons.
   Google Scholar
• Thomaidis, N. S., Kondakis, N., & Dounias, G. D. (2006). An intelligent statistical arbitrage trading system. Hellenic Conference on Artificial Intelligence, pp. 596–599. Berlin, Heidelberg: Springer-Verlag.
   Google Scholar
• Zapart, C. (2003). Statistical arbitrage trading with wavelets and artificial neural networks. IEEE International Conference on Computational Intelligence for Financial Engineering, Hong Kong, 20–23 March 2003, pp. 429–435.
   Google Scholar