On detecting and modeling periodic correlation in financial data
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.
|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://econwpa.repec.org|
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- 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.
- 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.
- 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.
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