Bayesian calibration and number of jump components in electricity spot price models

Jhonny Gonzalez, John Moriarty and Jan Palczewski

Energy Economics, 2017, vol. 65, issue C, 375-388

Abstract: We find empirical evidence that mean-reverting jump processes are not statistically adequate to model electricity spot price spikes but independent, signed sums of such processes are statistically adequate. Further we demonstrate a change in the composition of these sums after a major economic event. This is achieved by developing a Markov Chain Monte Carlo (MCMC) procedure for Bayesian model calibration and a Bayesian assessment of model adequacy (posterior predictive checking). In particular we determine the number of signed mean-reverting jump components required in the APXUK and EEX markets, in time periods both before and after the recent global financial crises. Statistically, consistent structural changes occur across both markets, with a reduction of the intensity and size, or the disappearance, of positive price spikes in the later period. All code and data are provided to enable replication of results.

Keywords: Multi-factor models; Bayesian calibration; Markov Chain Monte Carlo; Ornstein-Uhlenbeck process; Electricity spot price; Negative jumps (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C51 Q40 Q41 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:65:y:2017:i:c:p:375-388

DOI: 10.1016/j.eneco.2017.04.022

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