Probabilistic electricity price forecasting with Bayesian stochastic volatility models
By M. Kostrzewski, Jadwiga Kostrzewska. Energy Economics, 2019
61 citations recorded by the source corpus.
Abstract The study is focused on probabilistic forecasts of day-ahead electricity prices. The Bayesian approach allows for conducting statistical inference about model parameters, latent volatility, jump times and their sizes. Moreover, the Bayesian forecasting takes into account uncertainty of parameter estimation. Using the PJM data sets we demonstrate that Bayesian stochastic volatility model with double exponential distribution of jumps and exogenous variables outperforms the non-Bayesian individual autoregressive models as well as three averaging schemes of spot price forecasts. We argue that the structure is a promising tool of modelling and forecasting electricity prices.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/0ccc83a75abefa7d32a6a388d353110d35fd3896
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In Economics and Econometrics
- Florian Huber
- John M. Maheu
- Anastasios Panagiotelis
- Ruben Loaiza-Maya
- Gael M. Martin
- Didier Nibbering
- David T. Frazier
- Worapree Maneesoonthorn
In Electrical and Electronic Engineering
Citations in the corpus
- Bayesian forecasting in economics and finance: A modern review (2022, methodology): “(2017) and Kostrzewski and Kostrzewska (2019), where the latter show that Bayesian methods compare favourably to non-Bayesian alternatives for forecasting electricity prices. Kostrzewski and Kostrzewska (2019) also evaluate α-level quantile forecasts q̂t using the pinball loss, Lα(yt, q̂t) = α(yt − ”