IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v414y2014icp274-284.html
   My bibliography  Save this article

A new correlation coefficient for bivariate time-series data

Author

Listed:
  • Erdem, Orhan
  • Ceyhan, Elvan
  • Varli, Yusuf

Abstract

The correlation in time series has received considerable attention in the literature. Its use has attained an important role in the social sciences and finance. For example, pair trading in finance is concerned with the correlation between stock prices, returns, etc. In general, Pearson’s correlation coefficient is employed in these areas although it has many underlying assumptions which restrict its use. Here, we introduce a new correlation coefficient which takes into account the lag difference of data points. We investigate the properties of this new correlation coefficient. We demonstrate that it is more appropriate for showing the direction of the covariation of the two variables over time. We also compare the performance of the new correlation coefficient with Pearson’s correlation coefficient and Detrended Cross-Correlation Analysis (DCCA) via simulated examples.

Suggested Citation

  • Erdem, Orhan & Ceyhan, Elvan & Varli, Yusuf, 2014. "A new correlation coefficient for bivariate time-series data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 274-284.
    • Handle: RePEc:eee:phsmap:v:414:y:2014:i:c:p:274-284
    DOI: 10.1016/j.physa.2014.07.054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114006384
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2014.07.054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kristoufek, Ladislav, 2014. "Measuring correlations between non-stationary series with DCCA coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 291-298.
    2. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    3. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    4. 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.
    5. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Hailin, 2015. "Piecewise aggregate representations and lower-bound distance functions for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 10-25.
    2. Leena Pasanen & Lasse Holmström, 2017. "Scale space multiresolution correlation analysis for time series data," Computational Statistics, Springer, vol. 32(1), pages 197-218, March.
    3. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2017. "A novel weight determination method for time series data aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 42-55.
    4. Xiao Li & Liping Zhang & Sidong Zeng & Zhenyu Tang & Lina Liu & Qin Zhang & Zhengyang Tang & Xiaojun Hua, 2022. "Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    5. Marianna Brunetti & Roberta De Luca, 2020. "Pre-selection in Cointegration-based Pairs Trading," CEIS Research Paper 500, Tor Vergata University, CEIS, revised 10 Mar 2021.
    6. Sunil Kumar & Ilyoung Chong, 2018. "Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States," IJERPH, MDPI, vol. 15(12), pages 1-24, December.
    7. Contreras-Reyes, Javier E. & Idrovo-Aguirre, Byron J., 2020. "Backcasting and forecasting time series using detrended cross-correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).

    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. da Silva, Marcus Fernandes & Leão Pereira, Éder Johnson de Area & da Silva Filho, Aloisio Machado & Nunes de Castro, Arleys Pereira & Miranda, José Garcia Vivas & Zebende, Gilney Figueira, 2015. "Quantifying cross-correlation between Ibovespa and Brazilian blue-chips: The DCCA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 124-129.
    2. Kristoufek, Ladislav, 2014. "Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 169-175.
    3. Zhou, Yu & Chen, Shi, 2016. "Cross-correlation analysis between Chinese TF contracts and treasury ETF based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 117-127.
    4. Stefano Ferretti, 2023. "On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 969-1005, October.
    5. Kristoufek, Ladislav, 2015. "Finite sample properties of power-law cross-correlations estimators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 513-525.
    6. Li, Jianxuan & Shi, Yingying & Cao, Guangxi, 2018. "Topology structure based on detrended cross-correlation coefficient of exchange rate network of the belt and road countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1140-1151.
    7. Muhammad Zia Ullah Khan & Muhammad Illyas & Muqqadas Rahman & Chaudhary Abdul Rahman, 2015. "Money Monetization and Economic Growth in Pakistan," International Journal of Economics and Empirical Research (IJEER), The Economics and Social Development Organization (TESDO), vol. 3(4), pages 184-192, April.
    8. Yap, Wei Yim & Lam, Jasmine S.L., 2006. "Competition dynamics between container ports in East Asia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(1), pages 35-51, January.
    9. Francesca Iorio & Stefano Fachin, 2014. "Savings and investments in the OECD: a panel cointegration study with a new bootstrap test," Empirical Economics, Springer, vol. 46(4), pages 1271-1300, June.
    10. Md. Sharif Hossain & Md. Thasinul Abedin, 2016. "Multivariate Dynamic Co-integration and Causality Analysis between Inflation and its Determinants," Journal of Economics and Behavioral Studies, AMH International, vol. 8(5), pages 240-250.
    11. Longfeng Zhao & Wei Li & Andrea Fenu & Boris Podobnik & Yougui Wang & H. Eugene Stanley, 2017. "The q-dependent detrended cross-correlation analysis of stock market," Papers 1705.01406, arXiv.org, revised Jun 2017.
    12. Mr. Jacques Bouhga-Hagbe, 2004. "A Theory of Workers' Remittances with An Application to Morocco," IMF Working Papers 2004/194, International Monetary Fund.
    13. Bühler, Wolfgang & Korn, Olaf, 1998. "Hedging langfristiger Lieferverpflichtungen mit kurzfristigen Futures: möglich oder unmöglich?," ZEW Discussion Papers 98-20, ZEW - Leibniz Centre for European Economic Research.
    14. Ladislav Kristoufek & Paulo Ferreira, 2018. "Capital asset pricing model in Portugal: Evidence from fractal regressions," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 17(3), pages 173-183, November.
    15. Pär Österholm, 2005. "The Taylor Rule: A Spurious Regression?," Bulletin of Economic Research, Wiley Blackwell, vol. 57(3), pages 217-247, July.
    16. Zhai, Lu-Sheng & Liu, Ruo-Yu, 2019. "Local detrended cross-correlation analysis for non-stationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 222-233.
    17. Denise Côté & Christopher Graham, 2004. "Convergence of Government Bond Yields in the Euro Zone: The Role of Policy Harmonization," Staff Working Papers 04-23, Bank of Canada.
    18. Lee, Shih-Cheng & Lin, Chien-Ting, 2010. "An accounting-based valuation approach to valuing corporate governance in Taiwan," Journal of Contemporary Accounting and Economics, Elsevier, vol. 6(2), pages 47-60.
    19. Campos, Julia & Ericsson, Neil R. & Hendry, David F., 1996. "Cointegration tests in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 70(1), pages 187-220, January.
    20. Antràs Pol, 2004. "Is the U.S. Aggregate Production Function Cobb-Douglas? New Estimates of the Elasticity of Substitution," The B.E. Journal of Macroeconomics, De Gruyter, vol. 4(1), pages 1-36, April.

    More about this item

    Keywords

    Cross-correlation; Pearson’s correlation coefficient; DCCA; Stationarity; Non-stationary time series;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:eee:phsmap:v:414:y:2014:i:c:p:274-284. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.