Some statistical investigations on the nature and dynamics of electricity prices

Bottazzi, Giulio; Sapio, Sandro; Secchi, Angelo

doi:10.1016/j.physa.2005.02.065

Cited by 23 publications

(20 citation statements)

References 20 publications

(24 reference statements)

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“…In this case the short term price evolution can be mapped to a stochastic jump-diffusion model, applied among other models to non-durables [13]. The predicted fat tailed price distribution can be found for example in empirical investigations of the electricity spot price [17].…”

Section: Resultsmentioning

confidence: 99%

Evolutionary Model of Non-Durable Markets

Kaldasch

2012

SSRN Journal

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Presented is an evolutionary model of consumer non-durable markets, which is an extension of a previously published paper on consumer durables. The model suggests that the repurchase process is governed by preferential growth. Applying statistical methods it can be shown that in a competitive market the mean price declines according to an exponential law towards a natural price, while the corresponding price distribution is approximately given by a Laplace distribution for independent price decisions of the manufacturers. The sales of individual brands are determined by a replicator dynamics. As a consequence the size distribution of business units is a lognormal distribution, while the growth rates are also given by a Laplace distribution. Moreover products with a higher fitness replace those with a lower fitness according to a logistic law. Most remarkable is the prediction that the price distribution becomes unstable at market clearing, which is in striking difference to the Walrasian picture in standard microeconomics. The reason for this statement is that competition between products exists only if there is an excess supply, causing a decreasing mean price. When, for example by significant events, demand increases or is equal to supply, competition breaks down and the price exhibits a jump. When this supply shortage is accompanied with an arbitrage for traders, it may even evolve into a speculative bubble. Neglecting the impact of speculation here, the evolutionary model can be linked to a stochastic jump-diffusion model.

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Section: Resultsmentioning

confidence: 99%

Evolutionary Model of Non-Durable Markets

Kaldasch

2012

SSRN Journal

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“…There are also reports of heavy-tailed behavior of electricity prices. However, to our best knowledge, the studies were conducted either only for one market (Bottazzi et al, 2005, Byström, 2005, Eberlein and Stahl, 2003, Rachev et al, 2004, Weron, 2006, one distributional class (Mugele et al, 2005), samples of relatively small size (Deng and Jiang, 2005) or (log-)returns only (Chan andGray, 2006, Khindanova andAtakhanova, 2002). Especially the latter two limitations can lead to qualitatively different conclusions.…”

Section: Distributions Of Electricity Pricesmentioning

confidence: 99%

Heavy-tails and regime-switching in electricity prices

Weron

2008

Math Meth Oper Res

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In this paper we first analyze the stylized facts of electricity prices, in particular, the extreme volatility and price spikes which lead to heavy-tailed distributions of price changes. Then we calibrate Markov regime-switching (MRS) models with heavytailed components and show that they adequately address the aforementioned characteristics. Contrary to the common belief that electricity price models 'should be built on log-prices', we find evidence that modeling the prices themselves is more beneficial and methodologically sound, at least in case of MRS models.

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“…The knife-edge character of such a price setting mechanism is fatherly pushed to the extreme by a very low price elasticity of demand, and by technical constraints which time by time lead to network congestion (see e.g. [23,36] and references therein).…”

Section: Datamentioning

confidence: 99%

Predictability of price movements in deregulated electricity markets

Uritskaya

Uritsky

2015

Energy Economics

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In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti-persistent properties suggesting that their dynamics can be predicted based on historic price records across the range of time scales from one hour to one month. For both Canadian markets, the price movements reveal three types of correlated behavior which can be used for forecasting. The discovered scenarios remain the same on different time scales up to one month as well as for on-and off-peak electricity data. These scenarios represent sharp increases of prices and are not present in the Mid-C market due to its lower volatility. We argue that extreme price movements in this market should follow the same tendency as the more volatile Canadian markets. The estimated values of the Pareto indices suggest that the prediction of these events can be statistically stable. The results obtained provide new relevant information for managing financial risks associated with the dynamics of electricity derivatives over time frame exceeding one day.

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Some statistical investigations on the nature and dynamics of electricity prices

Cited by 23 publications

References 20 publications

Evolutionary Model of Non-Durable Markets

Evolutionary Model of Non-Durable Markets

Heavy-tails and regime-switching in electricity prices

Predictability of price movements in deregulated electricity markets

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