IDEAS home Printed from
   My bibliography  Save this paper

On detecting and modeling periodic correlation in financial data


  • Ewa Broszkiewicz-Suwaj

    (Wroclaw University of Technology)

  • Andrzej Makagon

    (Hampton University)

  • Rafal Weron

    (Hugo Steinhaus Center)

  • Agnieszka Wylomanska

    (Wroclaw University of Technology)


For many economic problems standard statistical analysis, based on the notion of stationarity, is not adequate. These include modeling seasonal decisions of consumers, forecasting business cycles and - as we show in the present article - modeling wholesale power market prices. We apply standard methods and a novel spectral domain technique to conclude that electricity price returns exhibit periodic correlation with daily and weekly periods. As such they should be modeled with periodically correlated processes. We propose to apply periodic autoregression (PAR) models which are closely related to the standard instruments in econometric analysis - vector autoregression (VAR) models.

Suggested Citation

  • Ewa Broszkiewicz-Suwaj & Andrzej Makagon & Rafal Weron & Agnieszka Wylomanska, 2005. "On detecting and modeling periodic correlation in financial data," Econometrics 0502006, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpem:0502006
    Note: Type of Document - pdf; pages: 12. Appeared in: Physica A 336 (2004) pp. 196-205

    Download full text from publisher

    File URL:
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Ewa Broszkiewicz-Suwaj, 2003. "Methods for determining the presence of periodic correlation based on the bootstrap methodology," HSC Research Reports HSC/03/02, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Wolf, Michael & Romano, Joseph P. & Politis, Dimitris N., 1999. "On the asymptotic theory of subsampling," DES - Working Papers. Statistics and Econometrics. WS 6334, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Benkwitz, Alexander, 2000. "Multiple time series analysis," SFB 373 Discussion Papers 2000,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. A. R. Nematollahi & A. R. Soltani & M. R. Mahmoudi, 2017. "Periodically correlated modeling by means of the periodograms asymptotic distributions," Statistical Papers, Springer, vol. 58(4), pages 1267-1278, December.
    2. Mohammad Reza Mahmoudi & Mohsen Maleki, 2017. "A new method to detect periodically correlated structure," Computational Statistics, Springer, vol. 32(4), pages 1569-1581, December.
    3. Anna E. Dudek, 2018. "Block bootstrap for periodic characteristics of periodically correlated time series," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 87-124, January.
    4. Ewa Broszkiewicz-Suwaj & Agnieszka Wylomanska, 2004. "Periodic correlation vs. integration and cointegration (Okresowa korelacja a integracja i kointegracja)," HSC Research Reports HSC/04/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. ŁUkasz Lenart & Jacek Leśkow & Rafał Synowiecki, 2008. "Subsampling in testing autocovariance for periodically correlated time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(6), pages 995-1018, November.
    6. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-22, August.
    7. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    8. Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Roohi, Reza, 2019. "A new method to compare the spectral densities of two independent periodically correlated time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 160(C), pages 103-110.
    9. Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S., 2013. "A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting," International Journal of Forecasting, Elsevier, vol. 29(1), pages 1-12.
    10. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, June.

    More about this item


    periodic correlation; sample coherence; electricity price; periodic autoregression; vector autoregression;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:wpa:wuwpem:0502006. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.