Probabilistic electricity price forecasting with Bayesian stochastic volatility models
Maciej Kostrzewski and
Energy Economics, 2019, vol. 80, issue C, 610-620
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.
Keywords: Electricity prices; Prediction intervals; Stochastic volatility process; Jumps (search for similar items in EconPapers)
JEL-codes: C51 C53 C58 Q41 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:80:y:2019:i:c:p:610-620
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