Skip to content
BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access June 17, 2012

Dynamics of episodic transient correlations in currency exchange rate returns and their predictability

Milan Žukovič
From the journal Open Physics

Abstract

We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer some potential for their predictability. We employ a rolling window approach in order to capture the correlation dynamics for different window lengths and analyze the distributions of periods with statistically significant correlations. We find that for sufficiently large window lengths these distributions fit well to power-law behavior. We also measure the predictability itself by a hit rate, i.e. the rate of consistency between the signs of the actual returns and their predictions, obtained from a simple correlation-based predictor. It is found that during these relatively brief periods the returns are predictable to a certain degree and the predictability depends on the selection of the window length.

[1] A. W. Lo, Econometrica 59, 1279 (1991) http://dx.doi.org/10.2307/293836810.2307/2938368Search in Google Scholar

[2] C. Eom, S. Choi, G. Oh, W. S. Yung, Physica A 387, 4630 (2008) http://dx.doi.org/10.1016/j.physa.2008.03.03510.1016/j.physa.2008.03.035Search in Google Scholar

[3] C. Eom, G. Oh, W. S. Yung, Physica A 387, 5511 (2008) http://dx.doi.org/10.1016/j.physa.2008.05.05910.1016/j.physa.2008.05.059Search in Google Scholar

[4] H. E. Hurst, Trans. Am. Soc. Civil. Eng. 116, 770 (1951) 10.1061/TACEAT.0006518Search in Google Scholar

[5] S. M. Pincus, Proc. Natl. Acad. Sci. USA 88, 2297 (1991) http://dx.doi.org/10.1073/pnas.88.6.229710.1073/pnas.88.6.2297Search in Google Scholar PubMed PubMed Central

[6] E. F. Fama, J. Bus. 38, 34 (1965) http://dx.doi.org/10.1086/29474310.1086/294743Search in Google Scholar

[7] C. W. J. Granger, O. Morgenstern, Kyklos 16, 1 (1963) http://dx.doi.org/10.1111/j.1467-6435.1963.tb00270.x10.1111/j.1467-6435.1963.tb00270.xSearch in Google Scholar

[8] M. J. Hinich, D. M. Patterson, J. Bus. Econ. Statist. 3, 69 (1985) 10.2307/1391691Search in Google Scholar

[9] K. P. Lim, R. D. Brooks, J. Econ. Surveys 25, 69 (2011) http://dx.doi.org/10.1111/j.1467-6419.2009.00611.x10.1111/j.1467-6419.2009.00611.xSearch in Google Scholar

[10] A. Serletis, A. G. Malliaris, M. J. Hinich, P. Gogas, Open Econ. Rev, DOI: 10.1007/s11079-010-9194-9 10.1007/s11079-010-9194-9Search in Google Scholar

[11] C. Brooks, M. J. Hinich, J. Empir. Finance 20, 385 (1999) http://dx.doi.org/10.1016/S0927-5398(99)00007-910.1016/S0927-5398(99)00007-9Search in Google Scholar

[12] C. A. Bonilla, R. Romero-Meza, M. J. Hinich, Appl. Econ. Letters 13, 195 (2006) http://dx.doi.org/10.1080/1350485050039275010.1080/13504850500392750Search in Google Scholar

[13] C. A. Bonilla, R. Romero-Meza, M. J. Hinich, Appl. Econ. 39, 2529 (2007) http://dx.doi.org/10.1080/0003684060070731610.1080/00036840600707316Search in Google Scholar

[14] M. J. Hinich, J Nonparametric Statist. 6, 205 (1996) http://dx.doi.org/10.1080/1048525960883267210.1080/10485259608832672Search in Google Scholar

[15] M. J. Hinich, D. M. Patterson, Mimeo, University of Texas at Austin (1995) Search in Google Scholar

[16] M. Hinich, D. M. Patterson, In: M. T. Belongia and J. M. Binner (Eds.), Money, Measurement and Computation, (Palgrave Macmillan, London, 2005) 61 Search in Google Scholar

[17] D. O. Cajueiro, B. M. Tabak, Chaos, Solitons & Fractals 22, 349 (2004) http://dx.doi.org/10.1016/j.chaos.2004.02.00510.1016/j.chaos.2004.02.005Search in Google Scholar

[18] D. O. Cajueiro, B. M. Tabak, Physica A 336, 521 (2004) http://dx.doi.org/10.1016/j.physa.2003.12.03110.1016/j.physa.2003.12.031Search in Google Scholar

[19] J. Alvarez-Ramirez, J. Alvarez, E. Rodriguez, G. Fernandez-Anaya, Physica A 387, 6159 (2008) http://dx.doi.org/10.1016/j.physa.2008.06.05610.1016/j.physa.2008.06.056Search in Google Scholar

[20] Y. Wang, L. Liu, R. Gu, J. Cao, H. Wang, Physica A 389, 1635 (2010) http://dx.doi.org/10.1016/j.physa.2009.12.03910.1016/j.physa.2009.12.039Search in Google Scholar

[21] K. P. Lim, Physica A 376, 445 (2007) http://dx.doi.org/10.1016/j.physa.2006.10.01310.1016/j.physa.2006.10.013Search in Google Scholar

[22] K. P. Lim, R. D. Brooks, J. H. Kim, Int. Rev. Finan. Anal. 17, 571 (2008) http://dx.doi.org/10.1016/j.irfa.2007.03.00110.1016/j.irfa.2007.03.001Search in Google Scholar

[23] K. P. Lim, R. D. Brooks, Chaos, Solitons and Fractals 40, 1271 (2009) http://dx.doi.org/10.1016/j.chaos.2007.09.00110.1016/j.chaos.2007.09.001Search in Google Scholar

[24] C. Brooks, M. J. Hinich, Appl. Econ. Letters 5, 719 (1998) http://dx.doi.org/10.1080/13504859835420310.1080/135048598354203Search in Google Scholar

[25] C. M. Jarque, A. K. Bera, Econ. Letters 6, 255 (1980) http://dx.doi.org/10.1016/0165-1765(80)90024-510.1016/0165-1765(80)90024-5Search in Google Scholar

[26] H. Lutkepohl, Introduction to Multiple Time Series Analysis, 2nd edition, (Springer-Verlag, Berlin, 1993) http://dx.doi.org/10.1007/978-3-642-61695-210.1007/978-3-642-61695-2Search in Google Scholar

[27] A. Neumaier, T. Schneider, ACM Transactions on Mathematical Software 27, 58 (2001) http://dx.doi.org/10.1145/382043.38230410.1145/382043.382304Search in Google Scholar

[28] A. Clauset, C. R. Shalizi, M. E. J. Newman, SIAM Review 51, 661 (2009) http://dx.doi.org/10.1137/07071011110.1137/070710111Search in Google Scholar

[29] D. Sornette, V. F. Pisarenko, Physica D: Nonlinear Phenomena 237, 429 (2008) http://dx.doi.org/10.1016/j.physd.2007.08.02010.1016/j.physd.2007.08.020Search in Google Scholar

Published Online: 2012-6-17
Published in Print: 2012-6-1

© 2012 Versita Warsaw

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Scroll Up Arrow