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On detecting and modeling periodic correlation in financial data

Author

Listed:
  • Ewa Broszkiewicz-Suwaj

    (Wroclaw University of Technology)

  • Andrzej Makagon

    (Hampton University)

  • Rafal Weron

    (Hugo Steinhaus Center)

  • Agnieszka Wylomanska

    (Wroclaw University of Technology)

Abstract

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
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0502/0502006.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. repec:spr:stpapr:v:58:y:2017:i:4:d:10.1007_s00362-016-0748-9 is not listed on IDEAS
    5. 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.
    6. 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.
    7. repec:spr:compst:v:32:y:2017:i:4:d:10.1007_s00180-016-0705-z is not listed on IDEAS
    8. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.

    More about this item

    Keywords

    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

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