Understanding the Fine Structure of Electricity Prices*

Geman, Hélyette; Roncoroni, Andréa

doi:10.1086/500675

Cited by 345 publications

(232 citation statements)

References 28 publications

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“…We denote by F := (F t ) t≥0 the filtration generated by (B t ) t≥0 and augmented by P-null sets. As in [7], the spot price of electricity X follows a standard time-homogeneous Ornstein-Uhlenbeck process with positive volatility σ , positive adjustment rate θ and positive asymptotic (or equilibrium) value μ; i.e., X x is the unique strong solution of Note that this model allows negative prices, which is consistent with the requirement to balance supply and demand in real time in electrical power systems and also consistent with the observed prices in several electricity spot markets (see, e.g., [12,18]). We denote by the random time of a consumer's demand for electricity.…”

Section: Problem Formulationsupporting

confidence: 70%

Optimal entry to an irreversible investment plan with non convex costs

et al. 2017

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A problem of optimally purchasing electricity at a real-valued spot price (that is, allowing negative prices) has been recently addressed in De Angelis et al. (SIAM J Control Optim 53(3), 1199-1223, 2015. The problem can be considered one of irreversible investment with a cost function which is non convex with respect to the control variable. In this paper we study optimal entry into the investment plan. The optimal entry policy can have an irregular boundary, with a kinked shape.Keywords Continuous-time inventory · Optimal stopping · Singular stochastic control · Irreversible investment · Ornstein-Uhlenbeck price process

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Section: Problem Formulationsupporting

confidence: 70%

Optimal entry to an irreversible investment plan with non convex costs

et al. 2017

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“…Finally, Knittel and Roberts (2005) find an inverse leverage effect for electricity prices in the United States. Other studies have found similar results (see, for example, Weron 2006Weron , 2008Harris 2006;Geman and Roncoroni 2006;Koopman et al 2007;Pilipović 2007;Sotiriadis et al 2016).…”

Section: The Electricity System Price For the Nordic Spot Electricitysupporting

confidence: 65%

“…Financial models use historical price series, and assuming stationarity, we can extract reliable characteristics for both the mean and volatility. Spot electricity prices exhibit high volatility, strong mean reversion (see, for example, Lucia and Schwartz 2002;Geman and Roncoroni 2006), frequent spikes and seasonal patterns (see Higgs and Worthington 2008;Huisman and Mahieu 2003;Thomas et al 2011) and differ from region to region (Li and Flynn 2004). Moreover, Goto and Karolyi (2004) find a mean-reversion effect with seasonal changes in volatilities as well as volatility clustering for electricity trading hubs in the United States, Australia and the Nordic/Baltic market.…”

Section: The Electricity System Price For the Nordic Spot Electricitymentioning

confidence: 99%

The Nordic/Baltic spot electric power system price: univariate nonlinear impulse-response analysis

Solibakke¹

2018

JEM

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This paper revisits the conditional mean and volatility density characteristics of the system price settled by the Nordic/Baltic spot electric power market . The main aim of this paper is an analysis of the nonlinear impulse-response features (shocks) in the nonstorable commodity market. Initially, we extract all deterministic seasonality and nonstationary trend and scale features from the series. A strictly stationary model reports serial correlation for the mean, and clustering, asymmetry and level effects for the volatility. For the mean, the impulse-response analysis reports linear and symmetric mean reversion for any price movements. For the volatility, small price movements show symmetric and decreasing volatility. In contrast, for larger absolute price movements, the volatility shows a nonlinear increase as well as fast-growing negative asymmetries. The impulse persistence is therefore relatively short. With the entrance of renewables into the energy market, the subperiod 2008-17 reports major systematic changes in the mean, volatility, asymmetry and persistence. In fact, the renewables era has changed the fundamental features of the Nordic/Baltic electricity market.

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“…In contrast, a jump regime switching model was developed in [7], which uses the hypothesis that log spot price switches between multiple linear Gaussian processes with a constant one period transition probability matrix. A regime switching threshold is also used by Geman and Roncoroni in [8] and [9] to force negative jumps if the price exceeds the threshold value. The authors in [8] claim that this model structure captures both trajectorial and statistical properties of US electricity price data well.…”

Section: Introductionmentioning

confidence: 99%

Electricity futures price models: Calibration and forecasting

Islyaev

Date

2015

European Journal of Operational Research

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A new one factor model with a random volatility parameter is presented in this paper for pricing of electricity futures contracts. It is shown that the model is more tractable than multi-factor jump diffusion models and yields an approximate closed-form pricing formula for the electricity futures prices. On real market data, it is shown that the performance of the new model compares favorably with two existing models in the literature, viz. a two factor jump diffusion model and its jump free version, i.e., a two factor linear Gaussian model, in terms of ability to predict one day ahead futures prices. Further, a multi-stage procedure is suggested and implemented for calibration of the two factor jump diffusion model, which alleviates the difficulty in calibration due to a large number of parameters and pricing formulae which involve numerical evaluation of integrals. We demonstrate the utility of our new model, as well as the utility of the calibration procedure for the existing two factor jump diffusion model, by model calibration and price forecasting experiments on three different futures price data sets from Nord pool electricity data. For the jump diffusion model, we also investigate empirically whether it performs better in terms of futures price prediction than a corresponding, jump-free linear Gaussian model. Finally, we investigate whether an explicit calibration of jump risk premium in the jump diffusion model adds value to the quality of futures price prediction. Our experiments do not yield any evidence that modelling jumps leads to a better price prediction in electricity markets.

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Understanding the Fine Structure of Electricity Prices*

Abstract: Birkbeck ePrints: an open access repository of the research output of Birkbeck College http://eprints.bbk.ac.uk

Cited by 345 publications

References 28 publications

Optimal entry to an irreversible investment plan with non convex costs

Optimal entry to an irreversible investment plan with non convex costs

The Nordic/Baltic spot electric power system price: univariate nonlinear impulse-response analysis

Electricity futures price models: Calibration and forecasting

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