IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1610.09812.html
   My bibliography  Save this paper

Long-range Correlation and Market Segmentation in Bond Market

Author

Listed:
  • Zhongxing Wang
  • Yan Yan
  • Xiaosong Chen

Abstract

This paper looks into the analysis of the long-range auto-correlations and cross-correlations in bond market. Based on Detrended Moving Average (DMA) method, empirical results present a clear evidence of long-range persistence that exists in one year scale. The degree of long-range correlation related to maturities has an upward tendency with a peak in short term. These findings confirm the expectations of fractal market hypothesis (FMH). Furthermore, we have developed a method based on a complex network to study the long-range cross-correlation structure and apply it to our data, and found a clear pattern of market segmentation in the long run. We also detected the nature of long-range correlation in the sub-period 2007 to 2012 and 2011 to 2016. The result from our research shows that long-range auto-correlations are decreasing in the recent years while long-range cross-correlations are strengthening.

Suggested Citation

  • Zhongxing Wang & Yan Yan & Xiaosong Chen, 2016. "Long-range Correlation and Market Segmentation in Bond Market," Papers 1610.09812, arXiv.org.
    • Handle: RePEc:arx:papers:1610.09812
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1610.09812
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kidwell, David S & Koch, Timothy W, 1983. "Market Segmentation and the Term Structure of Municipal Yields," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 15(1), pages 40-55, February.
    2. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, October.
    3. Cajueiro, Daniel O. & Tabak, Benjamin M., 2007. "Time-varying long-range dependence in US interest rates," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 360-367.
    4. Cajueiro, Daniel O. & Tabak, Benjamin M., 2007. "Long-range dependence and multifractality in the term structure of LIBOR interest rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 603-614.
    5. Backus, David K & Zin, Stanley E, 1993. "Long-Memory Inflation Uncertainty: Evidence from the Term Structure of Interest Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(3), pages 681-700, August.
    6. Hendershott, Patric H & Kidwell, David S, 1978. "The Impact of Relative Security Supplies: A Test with Data from a Regional Tax-Exempt Bond Market," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 10(3), pages 337-347, August.
    7. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "Testing for time-varying long-range dependence in volatility for emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 577-588.
    8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    9. Tabak, Benjamin M. & Cajueiro, Daniel O., 2005. "The long-range dependence behavior of the term structure of interest rates in Japan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 418-426.
    10. 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.
    11. Benson, Earl D. & Kidwell, David S. & Koch, Timothy W. & Rogowski, Robert J., 1981. "Systematic Variation in Yield Spreads for Tax-Exempt General Obligation Bonds," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 16(5), pages 685-702, December.
    12. Simon, David P., 1991. "Segmentation in the Treasury Bill Market: Evidence from Cash Management Bills," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 26(1), pages 97-108, March.
    13. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Carlos Echeverría, Juan, 2005. "Detrending fluctuation analysis based on moving average filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 199-219.
    14. Jiang, J. & Ma, K. & Cai, X., 2007. "Non-linear characteristics and long-range correlations in Asian stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 399-407.
    15. Gao-Feng Gu & Wei-Xing Zhou, 2010. "Detrending moving average algorithm for multifractals," Papers 1005.0877, arXiv.org, revised Jun 2010.
    16. Smith, Clifford Jr., 1976. "Option pricing : A review," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 3-51.
    17. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Zhongxing & Yan, Yan & Chen, Xiaosong, 2017. "Long-range correlation and market segmentation in bond market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 477-485.
    2. 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.
    3. Mensi, Walid & Tiwari, Aviral Kumar & Yoon, Seong-Min, 2017. "Global financial crisis and weak-form efficiency of Islamic sectoral stock markets: An MF-DFA analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 135-146.
    4. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Long memory volatility in Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1425-1433.
    5. Ruan, Yong-Ping & Zhou, Wei-Xing, 2011. "Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1646-1654.
    6. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    7. Gerlich, Nikolas & Rostek, Stefan, 2015. "Estimating serial correlation and self-similarity in financial time series—A diversification approach with applications to high frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 84-98.
    8. Laura Raisa Miloş & Cornel Haţiegan & Marius Cristian Miloş & Flavia Mirela Barna & Claudiu Boțoc, 2020. "Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets," Sustainability, MDPI, vol. 12(2), pages 1-15, January.
    9. Wang, Yudong & Liu, Li & Gu, Rongbao, 2009. "Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 271-276, December.
    10. Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla, 2014. "Multifractal detrended cross-correlation analysis of gold price and SENSEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 195-204.
    11. Lautier, Delphine & Raynaud, Franck, 2011. "Statistical properties of derivatives: A journey in term structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2009-2019.
    12. Ali, Sajid & Shahzad, Syed Jawad Hussain & Raza, Naveed & Al-Yahyaee, Khamis Hamed, 2018. "Stock market efficiency: A comparative analysis of Islamic and conventional stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 139-153.
    13. Liu, Li, 2014. "Cross-correlations between crude oil and agricultural commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 293-302.
    14. repec:dau:papers:123456789/5528 is not listed on IDEAS
    15. Jian Zhou & Gao-Feng Gu & Zhi-Qiang Jiang & Xiong Xiong & Wei Chen & Wei Zhang & Wei-Xing Zhou, 2017. "Computational Experiments Successfully Predict the Emergence of Autocorrelations in Ultra-High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 50(4), pages 579-594, December.
    16. Dutta, Srimonti & Ghosh, Dipak & Chatterjee, Sucharita, 2016. "Multifractal detrended Cross Correlation Analysis of Foreign Exchange and SENSEX fluctuation in Indian perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 188-201.
    17. Ruan, Qingsong & Zhang, Manqian & Lv, Dayong & Yang, Haiquan, 2018. "SAD and stock returns revisited: Nonlinear analysis based on MF-DCCA and Granger test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1009-1022.
    18. Zhou, Weijie & Dang, Yaoguo & Gu, Rongbao, 2013. "Efficiency and multifractality analysis of CSI 300 based on multifractal detrending moving average algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1429-1438.
    19. Akash P. POOJARI & Siva Kiran GUPTHA & G Raghavender RAJU, 2022. "Multifractal analysis of equities. Evidence from the emerging and frontier banking sectors," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(632), A), pages 61-80, Autumn.
    20. Wei, Yu & Chen, Wang & Lin, Yu, 2013. "Measuring daily Value-at-Risk of SSEC index: A new approach based on multifractal analysis and extreme value theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2163-2174.
    21. Rizvi, Syed Aun R. & Dewandaru, Ginanjar & Bacha, Obiyathulla I. & Masih, Mansur, 2014. "An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 86-99.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1610.09812. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.