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A New Correlation Coefficient for Bivariate Time-Series Data


  • Orhan Erdem

    (Istanbul Bilgi University)

  • Elvan Ceyhan

    (Koc University)

  • Yusuf Varlı

    (Istanbul Bilgi University)


Correlation in time series has recently recieved a lot of attentions. Its usage has been getting an important role in Social Science and Finance. For example, pair trading in Finance is interested with the correlation between stock prices, returns etc. In general, Pearsonís correlation coefficient is seen in the area, although it has many assumptions which restrict its usage. In here, we introduce a new correlation coe¢ cient which takes account the lag difference of data points as a moment. It is more convenient to show the the direction of the movements of the two variables over time. We also simulate the main differences between Pearson's and our correlation coe¢ cients in some cases.

Suggested Citation

  • Orhan Erdem & Elvan Ceyhan & Yusuf Varlı, 2011. "A New Correlation Coefficient for Bivariate Time-Series Data," Working Papers 201101, Murat Sertel Center for Advanced Economic Studies, Istanbul Bilgi University.
  • Handle: RePEc:msc:wpaper:201101

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    References listed on IDEAS

    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.
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    3. 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.
    4. 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.
    5. 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.
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    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).

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    More about this item


    Asymptotic normality; consistency; cross-correlation; Pearsons correlation coe¢ cient; stock returns; stationarity.;
    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


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