A spot-forward model for electricity prices with regime shifts

Paraschiv, Florentina; Fleten, Stein–Erik; Schürle, Michael

doi:10.1016/j.eneco.2014.11.003

Cited by 57 publications

(38 citation statements)

References 24 publications

(42 reference statements)

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“…Like in [12,18,[25][26][27]30], the modeling is implemented separately across the hours, leading to 24 sets of parameters for each day the forecasting exercise is performed. As Ziel [18] notes, when we compare the forecasting performance of relatively simple models implemented separately across the hours and jointly for all hours (like in [9,[34][35][36]), the latter generally performs better for the first half of the day, whereas the former are better in the second half of the day. At the same time, models implemented separately across the hours offer more flexibility by allowing for time-varying cross-hour dependency in a straightforward manner.…”

Section: Methodsmentioning

confidence: 99%

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

2016

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Abstract:In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall.

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Section: Methodsmentioning

confidence: 99%

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

2016

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“…The authors in [8] claim that this model structure captures both trajectorial and statistical properties of US electricity price data well. More recently, a regime switching model with different regimes for positive and negative spikes has been proposed in [10] for hourly price forward curves. A regime swiching model is also used in [11] for pricing energy commodity futures, where the authors do not use commodity spot price process and model the evolution of arbitrage-free futures price process directly.…”

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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“…4 Several models for electricity prices have been proposed in the literature (see e.g. [3,11,19,20,25,36] and the book [2] for a presentation and critical discussion of various models), and it would not be feasible to present RO pricing formulae for each one of these. For this reason, we choose a set of simple and significant ones, and present semi-explicit pricing formulae that have clear economic interpretations.…”

Section: Introductionmentioning

confidence: 99%

Pricing Reliability Options Under Different Electricity Prices’ Regimes

Andreis

Flora

Fontini³

et al. 2019

SSRN Journal

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Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first derived by means of closed-form pricing formulae, which are obtained under several assumptions about the dynamics of electricity prices and strike prices. Then, the value of the Reliability Option is simulated under a realmarket calibration, using data of the Italian power market. We finally perform sensitivity analyses to highlight the impact of the level and volatility of both power and strike price, of the mean reversion speeds and of the correlation coefficient on the Reliability Options' value.10 Interestingly enough, this result solves also a problem firstly posed in [24], in the framework of firms' evaluations. 11 As mentioned, this is going to be the case of the future Italian RO scheme.

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A spot-forward model for electricity prices with regime shifts

Cited by 57 publications

References 24 publications

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

Electricity futures price models: Calibration and forecasting

Pricing Reliability Options Under Different Electricity Prices’ Regimes

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