Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality

By Á. Cartea, Marcelo G. Figueroa. 2005

507 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering, Finance

View Paper: https://www.semanticscholar.org/paper/7c6d3260bdbf4ba499441975218a48317ec0d333

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In Computational Theory and Mathematics

In Electrical and Electronic Engineering

In Finance

Citations in the corpus

Modelling energy spot prices by volatility modulated Levy-driven Volterra processes (2013, methodology): “By letting L be a Lévy process with the pure-jump part given as a compound Poisson process, Cartea and Figueroa [40] successfully fitted the Schwartz model to electricity spot prices in the UK market.”
Electricity markets around the world (2018, methodology): “…filter’ technique, where prices corresponding to the price increments or returns exceeding three standard deviations of all returns are removed one by one in an iterative procedure, see, e.g., Clewlow and Strickland (2000); Weron et al. (2004b); Cartea and Figueroa (2005); Bierbrauer et al. (2007).”
Spot Price Modeling and the Valuation of Electricity Forward Contracts: The Role of Demand and Capacity (2007, unknown): “Similarly, Cartea and Figueroa (2005) present a mean-reverting jump-diffusion model of wholesale electricity prices and derive closed-form formulæ for forward contracts and apply the model to the E&W power market.”
Modelling spikes and pricing swing options in electricity markets (2009, background): “Calibration to historical data and the observed forward curve is discussed by Cartea and Figueroa ( 2005 ) where practical results for the UK electricity market are given.”
Stochastic modeling of financial electricity contracts (2008, background): “[11], Cartea and Figueroa [12], Elliott, Sick and Stein [17], Kaaresen and Husby [32] and Vehviläinen and Pyykkönnen [56].”
Electricity prices, large-scale renewable integration, and policy implications (2017, background): “We note that electricity prices during all hours and peak hours are not very informative regarding their unit root properties, although they should be stationary based on their mean reverting behavior [see Schwartz, 1997; Simonsen et al., 2004; Weron et al., 2004; Cartea and Figueroa, 2005], which i”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “• sinusoidal functions or sums of sinusoidal functions of different frequencies (Benth et al., 2012; Bierbrauer et al., 2007; Cartea and Figueroa, 2005; De Jong, 2006; Geman and Roncoroni, 2006; Seifert and Uhrig-Homburg, 2007; Weron, 2008),”
Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices (2018, methodology): “Unlike Weron (2008) and Cartea and Figueroa (2005), where the jump component is first estimated and then an OU process is assumed in the continuous part; we first estimate the mean model and then we assume a MRJD structure in the residuals. Weron (2014) offers a good review of MRJD models applied to”
An Intelligent Battery Controller Using Bias-Corrected Q-learning (2012, unknown): “We use a mean-reverting jump diffusion model proposed in (Cartea and Figueroa 2005) for our experimental work, which models the log of the price process Y t = log (P c + c) where c is an appropriately chosen shift constant to avoid zero or negative prices.”
Electricity price modeling with stochastic time change (2017, unknown): “The so-called reduced-form models range from jump-diffusions or Levy-driven diffusions (Geman and Roncoroni [16], Cartea and Figueroa [12], Meyer-Brandis and Tankov [26], Klüppelberg et al.”