MCMC calibration of spot‐prices models in electricity markets

Alice Guerini, Andrea Marziali and Giuseppe De Nicolao

Applied Stochastic Models in Business and Industry, 2020, vol. 36, issue 1, 62-76

Abstract: The calibration of some stochastic differential equation used to model spot prices in electricity markets is investigated. As an alternative to relying on standard likelihood maximization, the adoption of a fully Bayesian paradigm is explored, that relies on Markov chain Monte Carlo (MCMC) stochastic simulation and provides the posterior distributions of the model parameters. The proposed method is applied to one‐ and two‐factor stochastic models, using both simulated and real data. The results demonstrate good agreement between the maximum likelihood and MCMC point estimates. The latter approach, however, provides a more complete characterization of the model uncertainty, an information that can be exploited to obtain a more realistic assessment of the forecasting error. In order to further validate the MCMC approach, the posterior distribution of the Italian electricity price volatility is explored for different maturities and compared with the corresponding maximum likelihood estimates.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1002/asmb.2471

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:wly:apsmbi:v:36:y:2020:i:1:p:62-76

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().