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Research Papers

Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500

Pages 921-935 | Received 23 Feb 2018, Accepted 12 Oct 2018, Published online: 14 Nov 2018

References

  • Aït-Sahalia, Y., Jacod, J. and Li, J., Testing for jumps in noisy high frequency data. J. Econom., 2012, 168, 207–222. doi: 10.1016/j.jeconom.2011.12.004
  • Aït-Sahalia, Y. and Xiu, D., Using principal component analysis to estimate a high dimensional factor model with high-frequency data. J. Econom., 2017, 201, 384–399. doi: 10.1016/j.jeconom.2017.08.015
  • Al-Naymat, G., Chawla, S. and Taheri, J., SparseDTW: A novel approach to speed up dynamic time warping. In Proceedings of the 8th Australasian Data Mining Conference, edited by P.J. Kennedy, K. Ong and P. Christen, pp. 117–127, 2009 (Australian Computer Society: Melbourne).
  • Alexander, C., Market Models: A Guide to Financial Data Analysis, 2001 (John Wiley & Sons: Chichester).
  • Arici, T., Celebi, S., Aydin, A.S. and Temiz, T.T., Robust gesture recognition using feature pre-processing and weighted dynamic time warping. Multimed. Tools Appl., 2014, 72, 3045–3062. doi: 10.1007/s11042-013-1591-9
  • Avellaneda, M. and Lee, J.H., Statistical arbitrage in the US equities market. Quant. Finance, 2010, 10, 761–782. doi: 10.1080/14697680903124632
  • Baek, C. and Elbeck, M., Bitcoins as an investment or speculative vehicle? A first look. Appl. Econ. Lett., 2015, 22, 30–34. doi: 10.1080/13504851.2014.916379
  • Berndt, D.J. and Clifford, J., Using dynamic time warping to finder patterns in time series. In Knowledge Discovery in Databases: Papers from the AAAI Workshop, edited by U.M. Fayyad and R. Uthurusamy, pp. 359–370, 1994 (AAAI Press: Menlo Park, CA).
  • Bollinger, J., Using Bollinger bands. Stocks Commod., 1992, 10, 47–51.
  • Bollinger, J., Bollinger on Bollinger Bands, 2001 (McGraw-Hill: New York, NY).
  • Bouoiyour, J., Selmi, R., Tiwari, A.K. and Olayeni, O.R., What drives bitcoin price. Econ. Bull., 2016, 36, 843–850.
  • Bowen, D.A. and Hutchinson, M.C., Pairs trading in the UK equity market: Risk and return. Eur. J. Financ., 2016, 22, 1363–1387. doi: 10.1080/1351847X.2014.953698
  • Chen, H., Chen, S.J. and Li, F., Empirical investigation of an equity pairs trading strategy. Working paper, Columbia University, 2012.
  • Cheng, H., Dai, Z., Liu, Z. and Zhao, Y., An image-to-class dynamic time warping approach for both 3D static and trajectory hand gesture recognition. Pattern Recogn., 2016, 55, 137–147. doi: 10.1016/j.patcog.2016.01.011
  • Chinthalapati, V.L., High frequency statistical arbitrage via the optimal thermal causal path. Working paper, University of Greenwich, 2012.
  • Chohan, U.W., Cryptocurrencies: A brief thematic review. Working paper, University of New South Wales, 2017.
  • Coelho, M.S., Patterns in financial markets: Dynamic time warping. Working paper, NOVA School of Business and Economics, 2012.
  • Cont, R., Empirical properties of asset returns: Stylized facts and statistical issues. Quant. Finance, 2001, 1, 223–236. doi: 10.1080/713665670
  • Cummins, M. and Bucca, A., Quantitative spread trading on crude oil and refined products markets. Quant. Finance, 2012, 12, 1857–1875. doi: 10.1080/14697688.2012.715749
  • Ding, H., Trajcevski, G., Scheuermann, P., Wang, X. and Keogh, E.J., Querying and mining of time series data: Experimental comparison of representations and distance measures. In Proceedings of the Proceedings of the VLDB Endowment, edited by H.V. Jagadish, pp. 1542–1552, 2008 (ACM: New York, NY).
  • Do, B. and Faff, R., Does simple pairs trading still work? Financ. Anal. J., 2010, 66, 83–95. doi: 10.2469/faj.v66.n4.1
  • Do, B. and Faff, R., Are pairs trading profits robust to trading costs? J. Financ. Res., 2012, 35, 261–287. doi: 10.1111/j.1475-6803.2012.01317.x
  • Dupas, R., Tavenard, R., Fovet, O., Gilliet, N., Grimaldi, C. and Gascuel-Odoux, C., Identifying seasonal patterns of phosphorus storm dynamics with dynamic time warping. Water Resour. Res., 2015, 51, 8868–8882. doi: 10.1002/2015WR017338
  • Endres, S. and Stübinger, J., Optimal trading strategies for Lévy-driven Ornstein-Uhlenbeck processes. FAU Discussion Papers in Economics (17), University of Erlangen-Nürnberg, 2017.
  • Endres, S. and Stübinger, J., Regime-switching modeling of high-frequency stock returns with Lévy jumps. FAU Discussion Papers in Economics (3), University of Erlangen-Nürnberg, 2018.
  • Fama, E.F. and French, K.R., Multifactor explanations of asset pricing anomalies. J. Financ., 1996, 51, 55–84. doi: 10.1111/j.1540-6261.1996.tb05202.x
  • Fama, E.F. and French, K.R., A five-factor asset pricing model. J. Financ. Econ., 2015, 116, 1–22. doi: 10.1016/j.jfineco.2014.10.010
  • Fu, C., Zhang, P., Jiang, J., Yang, K. and Lv, Z., A Bayesian approach for sleep and wake classification based on dynamic time warping method. Multimed. Tools Appl., 2017, 76, 17765–17784. doi: 10.1007/s11042-015-3053-z
  • Gatev, E., Goetzmann, W.N. and Rouwenhorst, K.G., Pairs trading: Performance of a relative-value arbitrage rule. Rev. Financ. Stud., 2006, 19, 797–827. doi: 10.1093/rfs/hhj020
  • Huck, N. and Afawubo, K., Pairs trading and selection methods: Is cointegration superior? Appl. Econ., 2015, 47, 599–613. doi: 10.1080/00036846.2014.975417
  • Ilzetzki, E., Mendoza, E.G. and Végh, C.A., How big (small?) are fiscal multipliers? J. Monet. Econ., 2013, 60, 239–254. doi: 10.1016/j.jmoneco.2012.10.011
  • Itakura, F., Minimum prediction residual principle applied to speech recognition. IEEE T. Acoust. Speech, 1975, 23, 67–72. doi: 10.1109/TASSP.1975.1162641
  • Jiao, L., Wang, X., Bing, S., Wang, L. and Li, H., The application of dynamic time warping to the quality evaluation of Radix Puerariae thomsonii: Correcting retention time shift in the chromatographic fingerprints. J. Chromatogr. Sci., 2014, 53, 968–973. doi: 10.1093/chromsci/bmu161
  • Juang, B.H., On the hidden Markov model and dynamic time warping for speech recognition—A unified view. Bell Labs Tech. J., 1984, 63, 1213–1243. doi: 10.1002/j.1538-7305.1984.tb00034.x
  • Keogh, E.J. and Pazzani, M.J., Scaling up dynamic time warping for datamining applications. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, edited by R. Ramakrishnan, S. Stolfo, R. Bayardo and I. Parsa, pp. 285–289, 2000 (ACM: New York, NY).
  • Keogh, E.J. and Ratanamahatana, C.A., Exact indexing of dynamic time warping. Knowl. Inf. Syst., 2005, 7, 358–386. doi: 10.1007/s10115-004-0154-9
  • Kim, J.H., Shamsuddin, A. and Lim, K.P., Stock return predictability and the adaptive markets hypothesis: Evidence from century-long US data. J. Empirical Financ., 2011, 18, 868–879. doi: 10.1016/j.jempfin.2011.08.002
  • Kim, S. and Baginski, M.E., A cross correlation-based stock forecasting model. Working paper, Auburn University Journal, 2016.
  • Kim, S. and Heo, J., Time series regression-based pairs trading in the Korean equities market. J. Exp. Theor. Artif. Intell., 2017, 29, 755–768. doi: 10.1080/0952813X.2016.1259265
  • Knoll, J., Stübinger, J. and Grottke, M., Exploiting social media with higher-order factorization machines: Statistical arbitrage on high-frequency data of the S&P 500. Quant. Finance, Forthcoming, 2018.
  • Kristoufek, L., What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. PLOS One, 2015, 10, 1–15. doi: 10.1371/journal.pone.0123923
  • Létourneau, P. and Stentoft, L., Refining the least squares Monte Carlo method by imposing structure. Quant. Finance, 2014, 14, 495–507. doi: 10.1080/14697688.2013.787543
  • Li, Q. and Clifford, G.D., Dynamic time warping and machine learning for signal quality assessment of pulsatile signals. Physiol. Meas., 2012, 33, 1491–1502. doi: 10.1088/0967-3334/33/9/1491
  • McFadden, D. and Train, K., Mixed MNL models for discrete response. J. Appl. Econom., 2000, 15, 447–470. doi: 10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1
  • Meng, H., Xu, H.C., Zhou, W.X. and Sornette, D., Symmetric thermal optimal path and time-dependent lead-lag relationship: Novel statistical tests and application to UK and US real-estate and monetary policies. Quant. Finance, 2017, 17, 959–977. doi: 10.1080/14697688.2016.1241424
  • Miao, G.J., High frequency and dynamic pairs trading based on statistical arbitrage using a two-stage correlation and cointegration approach. Int. J. Econ. Financ., 2014, 6, 96–110. doi: 10.5539/ijef.v6n3p96
  • Muda, L., Begam, M. and Elamvazuthi, I., Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. J. Comput., 2010, 2, 138–143.
  • Müller, M., Information Retrieval for Music and Motion, 2007 (Springer: Berlin).
  • Müller, M., Mattes, H. and Kurth, F., An efficient multiscale approach to audio synchronization. In Proceedings of the 7th International Conference on Music Information Retrieval, edited by G. Tzanetakis and H. Hoos, pp. 192–197, 2006 (University of Victoria: Victoria).
  • Myers, C. and Rabiner, L., A level building dynamic time warping algorithm for connected word recognition. IEEE T. Acoust. Speech, 1981, 29, 284–297. doi: 10.1109/TASSP.1981.1163527
  • Myers, C., Rabiner, L. and Rosenberg, A., Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE T. Acoust. Speech, 1980, 28, 623–635. doi: 10.1109/TASSP.1980.1163491
  • Nakamoto, S., Bitcoin: A peer-to-peer electronic cash system, 2008. Available online at: https://bitcoin.org.
  • Narayanan, A., Bonneau, J., Felten, E., Miller, A. and Goldfeder, S., Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, 2016 (Princeton University Press: Princeton, NJ).
  • Pearson, K., Note on regression and inheritance in the case of two parents. Proc. R. Soc. London, 1895, 58, 240–242. doi: 10.1098/rspl.1895.0041
  • Prager, R., Vedbrat, S., Vogel, C. and Watt, E.C., Got Liquidity? 2012 (BlackRock Investment Institute: New York, NY).
  • Prätzlich, T., Driedger, J. and Müller, M., Memory-restricted multiscale dynamic time warping. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, edited by Z. Ding, Z.Q. Luo and W. Zhang, pp. 569–573, 2016 (IEEE: Danvers, MA).
  • QuantQuote, QuantQuote market data and software, 2016. Available online at: https://quantquote.com.
  • R Core Team, stats: A language and environment for statistical computing. R package, 2017.
  • Rabiner, L. and Juang, B.H., Fundamentals of Speech Recognition, 1993 (Prentice Hall: Upper Saddle River, NJ).
  • Rad, H., Low, R.K.Y. and Faff, R., The profitability of pairs trading strategies: Distance, cointegration and copula methods. Quant. Finance, 2016, 16, 1541–1558. doi: 10.1080/14697688.2016.1164337
  • Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J. and Keogh, E.J., Searching and mining trillions of time series subsequences under dynamic time warping. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, edited by Q. Yang, pp. 262–270, 2012 (ACM: New York, NY).
  • Rath, T.M. and Manmatha, R., Word image matching using dynamic time warping. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, edited by C. Dyer and P. Perona, pp. 521–527, 2003 (IEEE: Danvers, MA).
  • Sakoe, H. and Chiba, S., Dynamic programming algorithm optimization for spoken word recognition. IEEE T. Acoust. Speech, 1978, 26, 43–49. doi: 10.1109/TASSP.1978.1163055
  • Salvador, S. and Chan, P., Toward accurate dynamic time warping in linear time and space. Intell. Data Anal., 2007, 11, 561–580. doi: 10.3233/IDA-2007-11508
  • Senin, P., Dynamic time warping algorithm review. Working paper, University of Hawaii at Manoa, 2008.
  • Silva, D. and Batista, G., Speeding up all-pairwise dynamic time warping matrix calculation. In Proceedings of the 16th SIAM International Conference on Data Mining, edited by S.C. Venkatasubramanian and M. Wagner, pp. 837–845, 2016 (Society for Industrial and Applied Mathematics: Philadelphia, PA).
  • Sornette, D. and Zhou, W.X., Non-parametric determination of real-time lag structure between two time series: The “optimal thermal causal path” method. Quant. Finance, 2005, 5, 577–591. doi: 10.1080/14697680500383763
  • S&P Dow Jones Indices, S&P gl+obal—Equity S&P 500 index, 2015. Available online at: https://us.spindices.com/indices/equity/sp-500.
  • Stübinger, J. and Bredthauer, J., Statistical arbitrage pairs trading with high-frequency data. Int. J. Econ. Financ. Issues, 2017, 7, 650–662.
  • Stübinger, J. and Endres, S., Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. Quant. Finance, 2018, 18, 1735–1751. doi: 10.1080/14697688.2017.1417624
  • Stübinger, J., Mangold, B. and Krauss, C., Statistical arbitrage with vine copulas. Quant. Finance, 2018, 18, 1831–1849. doi: 10.1080/14697688.2018.1438642
  • Vidyamurthy, G., Pairs Trading: Quantitative Methods and Analysis, 2004 (John Wiley & Sons: Hoboken, NJ).
  • Vlachos, M., Kollios, G. and Gunopulos, D., Discovering similar multidimensional trajectories. In Proceedings of the 18th International Conference on Data Engineering, edited by R. Agrawal and K. Dittrich, pp. 673–684, 2002 (IEEE: Washington, DC).
  • Voya Investment Management, The impact of equity market fragmentation and dark pools on trading and alpha generation, 2016. Available online at: https://investments.voya.com.
  • Wang, G.J., Xie, C., Han, F. and Sun, B., Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree. Physica A Stat. Mech. Appl., 2012, 391, 4136–4146. doi: 10.1016/j.physa.2012.03.036
  • Zhang, L., Mykland, P.A. and Aït-Sahalia, Y., A tale of two time scales: Determining integrated volatility with noisy high-frequency data. J. Am. Stat. Assoc., 2005, 100, 1394–1411. doi: 10.1198/016214505000000169
  • Zhang, Y., Adl, K. and Glass, J., Fast spoken query detection using lower-bound dynamic time warping on graphical processing units. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, edited by H. Sakai and T. Nishitani, pp. 5173–5176, 2012 (IEEE: Danvers, MA).
  • Zhou, W.X. and Sornette, D., Non-parametric determination of real-time lag structure between two time series: The “optimal thermal causal path” method with applications to economic data. J. Macroecon., 2006, 28, 195–224. doi: 10.1016/j.jmacro.2005.10.015

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