Density Forecasting for Electricity Prices under Tail Heterogeneity with the t-Riesz Distribution

Anne Opschoor, Dewi Peerlings, Luca Rossini and Andre Lucas
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Anne Opschoor: Vrije Universiteit Amsterdam
Dewi Peerlings: Vrije Universiteit Amsterdam

No 24-049/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: We introduce the vector-valued t-Riesz distribution for time series models of electricity prices. The t-Riesz distribution extends the well-known Multivariate Student’s t distribution by allowing for tail heterogeneity via a vector of degrees of freedom (DoF) parameters. The closed-form density expression allows for straightforward maximum likelihood estimation. A clustering approach for the DoF parameters is provided to reduce the number of parameters in higher dimensions. We apply the t- Riesz distribution to a 24-dimensional panel of Danish daily electricity prices over the period 2017-2024, considering each hour of the day as a separate coordinate. Results show that multivariate t-Riesz-based density forecasts improve significantly upon the standard Student’s t distribution and the t-copula. Further, the t-Riesz distribution produces superior implied univariate density forecasts during the afternoon for the distribution as a whole and during 8 a.m.- 8 p.m. in its left tail. Moreover, during crisis periods, this effect is even stronger and holds for almost every hour of the day. Finally, portfolio Value-at-Risk forecasts during the central hours of the day improve during crisis periods compared to the classical Student’s t distribution and the t- copula.

Keywords: multivariate distributions; (fat)-tail heterogeneity; (inverse) Riesz distribution; electricity prices (search for similar items in EconPapers)
JEL-codes: C1 C22 C53 (search for similar items in EconPapers)
Date: 2024-07-19
New Economics Papers: this item is included in nep-ecm, nep-ene and nep-ets
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