Abstract
Analysis of long-range dependence in financial time series was one of the initial steps of econophysics into the domain of mainstream finance and financial economics in the 1990s. Since then, many different financial series have been analysed using the methods standardly used outside of finance to deliver some important stylized facts of the financial markets. In the late 2000s, these methods have started being generalized to bivariate settings so that the relationship between two series could be examined in more detail. It was then only a single step from bivariate long-range dependence towards scale-specific correlations and regressions as well as power-law coherency as a unique relationship between power-law correlated series. Such rapid development in the field has brought some issues and challenges that need further discussion and attention. We shortly review the development and historical steps from long-range dependence to bivariate generalizations and connected methods, focus on its technical aspects and discuss problematic parts and challenges for future directions in this specific subfield of econophysics.
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References
J. Barunik, L. Kristoufek, On Hurst exponent estimation under heavy-tailed distributions. Physica A 389(18), 3844–3855 (2010)
J. Beran, Statistics for Long-Memory Processes. Monographs on Statistics and Applied Probability, vol. 61 (Chapman and Hall, New York, 1994)
T. Bollerslev, Generalized autoregressive conditional heteroskedasticity. J. Econ. 31(3), 307–327 (1986)
G. Box, G.M. Jenkins, G.C. Reinsel, Time Series Analysis: Forecasting and Control (Prentice-Hall, Upper Saddle River, 1994)
R. Cont, Empirical properties of asset returns: stylized facts and statistical issues. Quant. Financ. 1(2), 223–236 (2001)
R.F. Engle, Autoregressive conditional heteroskedasticity with estimates of variance of United Kingdom inflation. Econometrica 50(4), 987–1007 (1982)
D. Grech, Z. Mazur, Statistical properties of old and new techniques in detrended analysis of time series. Acta Phys. Pol. B 36, 2403–2413 (2005)
L.-Y. He, S.-P. Chen, A new approach to quantify power-law cross-correlation and its application to commodity markets. Physica A 390, 3806–3814 (2011)
H. Hurst, Long term storage capacity of reservoirs. Trans. Am. Soc. Eng. 116, 770–799 (1951)
J. Kantelhardt, S. Zschiegner, E. Koscielny-Bunde, A. Bunde, S. Havlin, E. Stanley, Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316(1–4), 87–114 (2002)
L. Kristoufek, Rescaled range analysis and detrended fluctuation analysis: finite sample properties and confidence intervals. AUCO Czech Econ. Rev. 4, 236–250 (2010)
L. Kristoufek, Mixed-correlated ARFIMA processes for power-law cross-correlations. Physica A 392, 6484–6493 (2013)
L. Kristoufek, Testing power-law cross-correlations: Rescaled covariance test. Eur. Phys. J. B 86 (2013). Art. 418
L. Kristoufek, Detrending moving-average cross-correlation coefficient: measuring cross-correlations between non-stationary series. Physica A 406, 169–175 (2014)
L. Kristoufek, Measuring correlations between non-stationary series with DCCA coefficient. Physica A 402, 291–298 (2014)
L. Kristoufek, Spectrum-based estimators of the bivariate Hurst exponent. Phys. Rev. E 90, 062802 (2014)
L. Kristoufek, Can the bivariate Hurst exponent be higher than an average of the separate Hurst exponents? Physica A 431, 124–127 (2015)
L. Kristoufek, Detrended fluctuation analysis as a regression framework: estimating dependence at different scales. Phys. Rev. E 91, 022802 (2015)
L. Kristoufek, Finite sample properties of power-law cross-correlations estimators. Physica A 491, 513–525 (2015)
L. Kristoufek, On the interplay between short- and long-term memory in the power-law cross-correlations setting. Physica A 421, 218–222 (2015)
L. Kristoufek, Power-law cross-correlations estimation under heavy tails. Commun. Nonlinear Sci. Numer. Simul. 40, 163–172 (2016)
L. Kristoufek, Fractal approach towards power-law coherency to measure cross-correlations between series. Commun. Nonlinear Sci. Numer. Simul. 50, 193–200 (2017)
I. Lobato, Consistency of the average cross-periodogram in long memory time series. J. Time Ser. Anal. 18, 137–155 (1997)
I. Lobato, A semiparametric two-step estimator in a multivariate long memory model. J. Econ. 90, 129–153 (1999)
B. Mandelbrot, The variation of some other speculative prices. J. Bus. 40(4), 393–413 (1967)
B. Mandelbrot, J. van Ness, Fractional Brownian motions, fractional noises and applications. SIAM Rev. 10(422), 422–437 (1968)
B. Mandelbrot, J. Wallis, Noah, Joseph and operational hydrology. Water Resour. Res. 4, 909–918 (1968)
R.N. Mantegna, H.E. Stanley, An Introduction to Econophysics (Cambridge University Press, Cambridge, 2000)
P. Oswiecimka, S. Drozdz, M. Forczek, S. Jadach, J. Kwapien, Detrended cross-correlation analysis consistently extended to multifractality. Phys. Rev. E 89, 023305 (2014)
C. Peng, S. Buldyrev, A. Goldberger, S. Havlin, M. Simons, H. Stanley, Finite-size effects on long-range correlations: implications for analyzing DNA sequences. Phys. Rev. E 47(5), 3730–3733 (1993)
C. Peng, S. Buldyrev, S. Havlin, M. Simons, H. Stanley, A. Goldberger, Mosaic organization of DNA nucleotides. Phys. Rev. E 49(2), 1685–1689 (1994)
B. Podobnik, D. Horvatic, A. Lam Ng, H. Stanley, P. Ivanov, Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes. Physica A 387, 3954–3959 (2008)
B. Podobnik, H. Stanley, Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys. Rev. Lett. 100, 084102 (2008)
G. Samorodnitsky, Long range dependence. Found. Trends® Stoch. Syst. 1(3), 163–257 (2006)
R. Sela, C. Hurvich, Computationally efficient methods for two multivariate fractionally integrated models. J. Time Ser. Anal. 30(6), 631–651 (2009)
R. Sela, C. Hurvich, The average periodogram estimator for a power law in coherency. J. Time Ser. Anal. 33, 340–363 (2012)
M. Taqqu, W. Teverosky, W. Willinger, Estimators for long-range dependence: an empirical study. Fractals 3(4), 785–798 (1995)
M. Taqqu, V. Teverovsky, On estimating the intensity of long-range dependence in finite and infinite variance time series, in A Practical Guide To Heavy Tails: Statistical Techniques and Applications (1996)
V. Teverovsky, M. Taqqu, W. Willinger, A critical look at Lo’s modified r/s statistic. J. Stat. Plan. Inference 80(1–2), 211–227 (1999)
F. Wang, G.-P. Liao, J.-H. Li, R.-B. Zou, W. Shi, Cross-correlation detection and analysis for California’s electricity market based on analogous multifractal analysis. Chaos 23, 013129 (2013)
G. Zebende, DCCA cross-correlation coefficient: quantifying level of cross-correlation. Physica A 390, 614–618 (2011)
Acknowledgements
Ladislav Kristoufek gratefully acknowledges financial support of the Czech Science Foundation (project 17-12386Y).
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Kristoufek, L. (2021). Power-Law Cross-Correlations: Issues, Solutions and Future Challenges. In: Grech, D., Miśkiewicz, J. (eds) Simplicity of Complexity in Economic and Social Systems. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-56160-4_3
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