Market structure and the stability and volatility of electricity prices

Bask, Mikael; Widerberg, Anna

doi:10.1016/j.eneco.2008.11.006

Cited by 18 publications

(9 citation statements)

References 35 publications

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“…With the dynamic market model, the role that futures markets may play on the clearing prices and on altering the volatility and potential instability of real-time prices and generator output was examined by Watts and Alvarado [12]. Reference [13] adopted a novel approach for system controllers characterizing Nordic power market, and it was concluded that electricity prices most of the time have increased in stability and decreased in volatility when the market has expanded and the competition degree has added. In [14], an appropriate modeling of system controllers is presented to account for the effects of power system controllers on the stability of power dispatch and electricity market prices better.…”

Section: Introductionmentioning

confidence: 99%

The Interval Stability of an Electricity Market Model

Wang

Zhu

2014

Mathematical Problems in Engineering

142

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Combined with the electric power market dynamic model put forward by Alvarado, an interval model of electricity markets is established and investigated in this paper pertaining to the range of demand elasticity with suppliers and consumers. The stability of an electricity market framework with demand elasticity interval is analyzed. The conclusions characterizing the interval model provided are derived by constructing a suitable Lyapunov function and using the theory of interval dynamical system in differential equations and matrix inequality theory and so forth. Applying the corollary obtained can judge the system stability by available data about demand elasticity. The obtained results are validated and illustrated by a case example.

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Section: Introductionmentioning

confidence: 99%

The Interval Stability of an Electricity Market Model

Wang

Zhu

2014

Mathematical Problems in Engineering

142

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“…Another example of strong interconnection coupled with increasing competition is the Nordic electricity market. Bask et al (2008) [15] applied a stochastic model to generate electricity prices at Nord Pool and then used a GARCH analysis to show that prices became less sensitive to external shocks while the Nordic power market was being expanded and the degree of competition was being increased.…”

Section: Literature Reviewmentioning

confidence: 99%

Granger Causality Network Methods for Analyzing Cross-Border Electricity Trading between Greece, Italy, and Bulgaria

et al. 2020

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Italy, Greece, and, to a lesser degree, Bulgaria have experienced fast growth in their renewable generation capacity (RESc) over the last several years. The consequences of this fact include a decrease in spot wholesale prices in electricity markets and a significant effect on cross border trading (CBT) among neighboring interconnected countries. In this work, we empirically analyzed historical data on fundamental market variables (i.e., spot prices, load, RES generation) as well as CBT data (imports, exports, commercial schedules, net transfer capacities, etc.) on the Greek, Italian, and Bulgarian electricity markets by applying the Granger causality connectivity analysis (GCCA) approach. The aim of this analysis was to detect all possible interactions among the abovementioned variables, focusing in particular on the effects of growing shares of RES generation on the commercial electricity trading among the abovementioned countries for the period 2015–2018. The key findings of this paper are summarized as the following: The RES generation in Italy, for the period examined, drives the spot prices in Greece via commercial schedules. In addition, on average, spot price fluctuations do not affect the commercial schedules of energy trading between Greece and Bulgaria.

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“…Even though Bask and Widerberg (2009) do not focus on the market risk and the potential market risk in the Nordic power market, they utilize the aforementioned methodology when examining how the integration process at this market has affected the stability and volatility of electricity prices. Specifically, the non-parametric approach outlined above is used when estimating the stability of electricity prices, whereas four different ARCH models are used when estimating the volatility of the same prices.…”

Section: Two Variations Of ( 2 )-Analysismentioning

confidence: 99%

“…An alternative approach to monitor an asset's potential market risk, which is in the spirit of the studies in Bask and de Luna (2005) and Bask and Widerberg (2009), is the following approach: let [t {s t−p 0 +1 , s t−p 0 +2 , . .…”

Section: Two Applications Of ( 2 )-Analysismentioning

confidence: 99%

Measuring potential market risk

Bask

2010

Journal of Financial Stability

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a b s t r a c tWe argue herein that there is a fundamental and an important difference between the market risk and the potential market risk in financial markets. We also argue that the spectrum of smooth Lyapunov exponents can be used in ( , 2 )-analysis, which is a method to measure and monitor these risks. The reason is that these exponents focus on the stability properties ( ) of the stochastic dynamic system generating asset returns, while more traditional risk measures such as value-at-risk are concerned with the distribution of asset returns ( 2 ).

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Market structure and the stability and volatility of electricity prices

Cited by 18 publications

References 35 publications

The Interval Stability of an Electricity Market Model

The Interval Stability of an Electricity Market Model

Granger Causality Network Methods for Analyzing Cross-Border Electricity Trading between Greece, Italy, and Bulgaria

Measuring potential market risk

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