On detecting and modeling periodic correlation in financial data

被引：62

作者：

Broszkiewicz-Suwaj, E

Makagon, A

Weron, R ^{[1
]}

Wylomanska, A

机构：

[1] Wroclaw Univ Technol, Hugo Steinhaus Ctr Stochast Methods, PL-50370 Wroclaw, Poland

[2] Hampton Univ, Dept Math, Hampton, VA 23668 USA

[3] Wroclaw Univ Technol, Inst Math, PL-50370 Wroclaw, Poland

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2004年 / 336卷 / 1-2期

关键词：

periodic correlation; sample coherence; electricity price; periodic autoregression; vector autoregression;

D O I：

10.1016/j.physa.2004.01.025

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：196 / 205

页数：10