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Modeling extreme dependence between European electricity markets

Lindström, Erik LU and Regland, Fredrik (2012) In Energy Economics 34(4). p.899-904
Abstract
Electricity spot prices are characterized by sudden large movements, followed a few days later by an equally large movement in the opposite direction. These phenomena are called spikes (upward movements) and drops (downward movements). Recent research has suggested that the dynamics of the electricity spot prices can be accurately described by hidden Markov Regime Switching (MRS) models. Regime switch models separate the ordinary dependence and the extreme (spike or drop) dependence. This is a crucial point since it is the extreme dependence that is of interest when computing risks. We fit a large number of MRS models to six European electricity markets (EEX, PowerNext, APX Power UK & NL, Nord Pool System & Sweden) in order to... (More)
Electricity spot prices are characterized by sudden large movements, followed a few days later by an equally large movement in the opposite direction. These phenomena are called spikes (upward movements) and drops (downward movements). Recent research has suggested that the dynamics of the electricity spot prices can be accurately described by hidden Markov Regime Switching (MRS) models. Regime switch models separate the ordinary dependence and the extreme (spike or drop) dependence. This is a crucial point since it is the extreme dependence that is of interest when computing risks. We fit a large number of MRS models to six European electricity markets (EEX, PowerNext, APX Power UK & NL, Nord Pool System & Sweden) in order to analyze the nature of the prices. The estimated regimes are then used to analyze the probability of an extreme event and the conditional probability for one market to experience an extreme event, conditional that another market is experiencing the same event. We find that the frequency of extreme events is positively related to the amount of renewable energy sources in the power system. It can be argued that the dependence results in this paper give an indication of what future dependence (for even more integrated markets with more renewable energy) will be. The integration of markets will lead to efficient sharing of reserves while the additional renewable energy will increase the need for reserves. The results are of interest when computing financial risks (e.g. VaR or expected shortfall), when designing reserves but also as an indication of the degree of integration between markets. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Electricity spot price, Hidden Markov Regime Switching models, Independent Spike models, Market integration
in
Energy Economics
volume
34
issue
4
pages
899 - 904
publisher
Elsevier
external identifiers
  • wos:000306158000005
  • scopus:84861203952
ISSN
0140-9883
DOI
10.1016/j.eneco.2012.04.006
language
English
LU publication?
yes
id
e4f5476b-7efc-4b08-95de-57b99bb46879 (old id 3008462)
date added to LUP
2012-09-21 19:44:22
date last changed
2017-08-20 03:03:54
@article{e4f5476b-7efc-4b08-95de-57b99bb46879,
  abstract     = {Electricity spot prices are characterized by sudden large movements, followed a few days later by an equally large movement in the opposite direction. These phenomena are called spikes (upward movements) and drops (downward movements). Recent research has suggested that the dynamics of the electricity spot prices can be accurately described by hidden Markov Regime Switching (MRS) models. Regime switch models separate the ordinary dependence and the extreme (spike or drop) dependence. This is a crucial point since it is the extreme dependence that is of interest when computing risks. We fit a large number of MRS models to six European electricity markets (EEX, PowerNext, APX Power UK & NL, Nord Pool System & Sweden) in order to analyze the nature of the prices. The estimated regimes are then used to analyze the probability of an extreme event and the conditional probability for one market to experience an extreme event, conditional that another market is experiencing the same event. We find that the frequency of extreme events is positively related to the amount of renewable energy sources in the power system. It can be argued that the dependence results in this paper give an indication of what future dependence (for even more integrated markets with more renewable energy) will be. The integration of markets will lead to efficient sharing of reserves while the additional renewable energy will increase the need for reserves. The results are of interest when computing financial risks (e.g. VaR or expected shortfall), when designing reserves but also as an indication of the degree of integration between markets.},
  author       = {Lindström, Erik and Regland, Fredrik},
  issn         = {0140-9883},
  keyword      = {Electricity spot price,Hidden Markov Regime Switching models,Independent Spike models,Market integration},
  language     = {eng},
  number       = {4},
  pages        = {899--904},
  publisher    = {Elsevier},
  series       = {Energy Economics},
  title        = {Modeling extreme dependence between European electricity markets},
  url          = {http://dx.doi.org/10.1016/j.eneco.2012.04.006},
  volume       = {34},
  year         = {2012},
}