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...

Full description

Saved in:
Bibliographic Details
Published inPhysica A Vol. 336; no. 1; pp. 196 - 205
Main Authors Broszkiewicz-Suwaj, E, Makagon, A, Weron, R, Wyłomańska, A
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.05.2004
Subjects
Electricity price
Periodic autoregression
Periodic correlation
Sample coherence
Vector autoregression
02.70.Hm
Vector autoregression
Sample coherence
89.65.Gh
05.45.Tp
Electricity price
Periodic correlation
Periodic autoregression
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2004.01.025