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Comparative analysis of grey detrended fluctuation analysis methods based on empirical research on China’s interest rate market

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  • Cao, Guangxi
  • Jiang, Min
  • He, LingYun

Abstract

This study empirically compares the development of China’s interest rate market in the past decade. The yield of Shibor market from 2006 to 2016 showed anti-persistence, and the later five-year trend was stronger than that in the previous half period, indicating a significantly growing trend of China’s interest rate market. We perform a comparative analysis of detrended fluctuation analysis (DFA), detrending moving average (DMA) algorithm, and the maximum overlap wavelet transform (DFA-MODWT) using grey and original sequences. The autoregressive integrated moving average model is adopted to generate set-point H sequences. Results illustrated that 1) for original sequences, both the DFA-MODWT and DMA (θ=0.5) shows outstanding performances when the set Hurst exponents are 0.6 and 0.7 and 0.8 and 0.9, respectively. 2) For grey sequences, G-DFA performs best among the three methods, followed by G-DMA and G-DFA-MODWT. 3) The long-range correlation of the original sequences is slightly lower than the set values. On the contrary, the long-range correlation of grey sequences is much higher than the set values. Thus, the grey sequence exerts a strong aggregation effect as it can accumulate the trend of the sequence. Our results show that the grey sequence can determine the regular pattern from the messy sequence. However, grey sequences tend to be overly analyzed and misleading when large volumes of data are involved. Empirical studies confirm that China’s interest rate market remains an inefficient market.

Suggested Citation

  • Cao, Guangxi & Jiang, Min & He, LingYun, 2018. "Comparative analysis of grey detrended fluctuation analysis methods based on empirical research on China’s interest rate market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 156-169.
    • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:156-169
    DOI: 10.1016/j.physa.2018.04.052
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    as
    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Henry, Guillermo & Rodriguez, Daniela, 2016. "On the instability of two entropic dynamical models," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 604-609.
    3. Yong Luo & Jie Xiong & Lie Gang Dong & Yong Tang, 2015. "Statistical correlation properties of the SHIBOR interbank lending market," China Finance Review International, Emerald Group Publishing Limited, vol. 5(2), pages 91-102, May.
    4. He, Ling-Yun & Chen, Shu-Peng, 2011. "A new approach to quantify power-law cross-correlation and its application to commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3806-3814.
    5. Gu, Rongbao & Chen, Xi & Li, Xinjie, 2014. "Chaos recognition and fractal analysis in the term structure of Shanghai Interbank Offered Rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 412(C), pages 101-112.
    6. McCulloch, James, 2012. "Fractal market time," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 686-701.
    7. Zebende, G.F. & da Silva, M.F. & Machado Filho, A., 2013. "DCCA cross-correlation coefficient differentiation: Theoretical and practical approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1756-1761.
    8. Cao, Guangxi & Xu, Wei, 2016. "Nonlinear structure analysis of carbon and energy markets with MFDCCA based on maximum overlap wavelet transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 505-523.
    9. He, Ling-Yun & Chen, Shu-Peng, 2010. "Are crude oil markets multifractal? Evidence from MF-DFA and MF-SSA perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3218-3229.
    10. James McCulloch, 2012. "Fractal Market Time," Research Paper Series 311, Quantitative Finance Research Centre, University of Technology, Sydney.
    11. He, Ling-Yun & Chen, Shu-Peng, 2011. "Multifractal Detrended Cross-Correlation Analysis of agricultural futures markets," Chaos, Solitons & Fractals, Elsevier, vol. 44(6), pages 355-361.
    12. Cao, Guangxi & Cao, Jie & Xu, Longbing, 2013. "Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 797-807.
    13. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    14. Duan, Qihong & Wei, Ying & Chen, Zhiping, 2014. "Relationship between the benchmark interest rate and a macroeconomic indicator," Economic Modelling, Elsevier, vol. 38(C), pages 220-226.
    15. Gao-Feng Gu & Wei-Xing Zhou, 2010. "Detrending moving average algorithm for multifractals," Papers 1005.0877, arXiv.org, revised Jun 2010.
    16. Eugene F. Fama, 1963. "Mandelbrot and the Stable Paretian Hypothesis," The Journal of Business, University of Chicago Press, vol. 36, pages 420-420.
    17. Qishui Chi & Shiwen Fu, 2016. "The Impact of the Interest Rate Liberalization on Both Banks and Small Firms: Evidence from China," Research in World Economy, Research in World Economy, Sciedu Press, vol. 7(2), pages 26-33, December.
    18. Dariusz Grech & Zygmunt Mazur, 2012. "On the scaling ranges of detrended fluctuation analysis for long-memory correlated short series of data," Papers 1206.1007, arXiv.org.
    19. Ying-Hui Shao & Gao Feng Gu & Zhi-Qiang Jiang & Wei-Xing Zhou & Didier Sornette, 2012. "Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series," Papers 1208.4158, arXiv.org.
    20. Dufour, Jean-Marie & Kurz-Kim, Jeong-Ryeol & Palm, Franz C., 2010. "Editorial introduction: Heavy tails and stable Paretian distributions in empirical finance: A volume honoring Benoît B. Mandelbrot," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 177-179, March.
    21. Paolella, Marc S., 2017. "Asymmetric stable Paretian distribution testing," Econometrics and Statistics, Elsevier, vol. 1(C), pages 19-39.
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