On detecting and modeling periodic correlation in financial data

Broszkiewicz-Suwaj, Ewa; Makagon, Andrzej; Weron, Rafał; Wyłomańska, Agnieszka

doi:10.1016/j.physa.2004.01.025

Cited by 70 publications

(36 citation statements)

References 21 publications

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“…Note that to some extent, this structure resembles periodic autoregressive moving average (PARMA) models, which have seen limited use in EPF [37,38]. Like for the ARX1 model, also for mARX1, we consider two variants:…”

Section: Autoregressive Expert Benchmarksmentioning

confidence: 99%

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

2016

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Abstract:In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall.

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Section: Autoregressive Expert Benchmarksmentioning

confidence: 99%

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

2016

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“…An alternative, but rarely utilized approach would be to use periodic time series, like Periodic Autoregressive Moving-Average (PARMA) models (Franses and Paap, 2004). Although electricity prices have been shown to exhibit periodic correlation (Broszkiewicz-Suwaj et al, 2004), the application of PARMA models is limited due to the computational burden involved.…”

Section: The Modelsmentioning

confidence: 99%

Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

Misiorek¹,

Trueck²,

Weron³

2006

Studies in Nonlinear Dynamics &Amp; Econometrics

154

151

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In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX ("X" stands for exogenous/fundamental variable -system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-ofsample point and interval forecasting in normal and extremely volatile periods preceding the market crash in winter 2000/2001. We find evidence that (i) non-linear, threshold regime-switching (TAR/TARX) models outperform their linear counterparts, both in point and interval forecasting, and that (ii) an additional GARCH component generally decreases point forecasting efficiency. Interestingly, the former result challenges a number of previously published studies on the failure of non-linear regime-switching models in forecasting. * The authors are grateful to Dick van Dijk and two anonymous referees for insightful comments and suggestions. This work was supported in part by KBN grant 4-T10B-030-25 (to Misiorek).

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“…Applications are considered in the following fields: acoustics and mechanics [10,14], radio astronomy and astrophysics [78,100,145], optics and spectroscopy [105,310,309], analysis of genome and biological signals [19,97,128,132,235], finance and econometrics [27,43,160,204], and climatology [84,125,151,190,308].…”

Section: Introductionmentioning

confidence: 99%