EconPapers    
Economics at your fingertips  
 

Probabilistic electricity price forecasting with Bayesian stochastic volatility models

Maciej Kostrzewski and Jadwiga Kostrzewska

Energy Economics, 2019, vol. 80, issue C, 610-620

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.

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)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988319300544
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:80:y:2019:i:c:p:610-620

Access Statistics for this article

Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-11-24
Handle: RePEc:eee:eneeco:v:80:y:2019:i:c:p:610-620