On detecting and modeling periodic correlation in financial data
AbstractFor 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0502006.
Length: 12 pages
Date of creation: 07 Feb 2005
Date of revision:
Note: Type of Document - pdf; pages: 12. Appeared in: Physica A 336 (2004) pp. 196-205
Contact details of provider:
Web page: http://18.104.22.168
periodic correlation; sample coherence; electricity price; periodic autoregression; vector autoregression;
Other versions of this item:
- Broszkiewicz-Suwaj, E & Makagon, A & Weron, R & Wyłomańska, A, 2004. "On detecting and modeling periodic correlation in financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 196-205.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull 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
- L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
- Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-04-16 (All new papers)
- NEP-ECM-2005-04-16 (Econometrics)
- NEP-ETS-2005-04-16 (Econometric Time Series)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA).
If references are entirely missing, you can add them using this form.