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|
|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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