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On detecting and modeling periodic correlation in financial data

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  • Broszkiewicz-Suwaj, E
  • Makagon, A
  • Weron, R
  • Wyłomańska, A

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 models which are closely related to the standard instruments in econometric analysis—vector autoregression models.

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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 336 (2004)
Issue (Month): 1 ()
Pages: 196-205

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Handle: RePEc:eee:phsmap:v:336:y:2004:i:1:p:196-205

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

Related research

Keywords: Periodic correlation; Sample coherence; Electricity price; Periodic autoregression; Vector autoregression;

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Cited by:
  1. 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.
  2. 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.

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