Factor models in the German electricity market: Stylized facts, seasonality, and calibration

Hinderks, Wieger; Wagner, Andreas

doi:10.1016/j.eneco.2019.03.024

Cited by 24 publications

(9 citation statements)

References 37 publications

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“…First of all, arithmetic dynamics allow to encompass the observed negativity of spot prices of electricity. The importance of taking into account such a stylized fact of electricity prices in the modeling is also confirmed by the increasing interest that arithmetic models are recently obtaining in the literature (see, e.g., Benth et al 2019;Edoli et al 2017;Fanone et al 2013;Hinderks and Wagner 2020;Latini et al 2019;Piccirilli et al 2021). Second of all, using arithmetic models one can obtain explicit closed form formulas for the price of the swap price, given by the time-average of the spot price over the delivery period.…”

Section: Introductionmentioning

confidence: 90%

The Effect of Mean-Reverting Processes in the Pricing of Options in the Energy Market: An Arithmetic Approach

Schmeck

Schwerin²

2021

Risks

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In this paper we study the effect that mean-reverting components in the arithmetic dynamics of electricity spot price have on the price of a call option on a swap. Our model allows for seasonal effects, spikes, and negative values of the price of electricity. We show that for sufficiently large delivery periods of the swap contract, the error that one makes by neglecting some of the mean-reverting processes affecting the spot price evolution converges to zero. The decay rate is explicitly calculated. This is achieved by exploiting the additive structure of the electricity price process in order to determine an explicit closed-form formula for the price of the call on a swap. The theoretical analysis is then illustrated via a numerical example.

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Section: Introductionmentioning

confidence: 90%

The Effect of Mean-Reverting Processes in the Pricing of Options in the Energy Market: An Arithmetic Approach

Schmeck

Schwerin²

2021

Risks

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“…For example, renewable energies can reduce price volatility in Hydrothermal power markets (70) . Thus, the bibliometric analysis showed that approximately 60% of the papers of the last five years had the price forecast as the main objective (29,66,(71)(72)(73)(74) . In this way, price prediction has become a principal method for planning and operations in energy systems.…”

Section: Historical Factorsmentioning

confidence: 99%

Determinantes del Precio de la Electricidad en Mercados No Regulados: Un Análisis Bibliométrico

Gómez

Londoño-Hernández

Manotas-Duque

2021

inycomp

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Los procesos de liberalización de los mercados eléctricos en diferentes países generaron una nueva forma de definir los precios de la electricidad, lo que ha aumentado la dificultad para modelar y analizar su dinámica. Este estudio presenta un análisis bibliométrico de los determinantes del precio de la electricidad en mercados no regulados. La investigación sistemática a través de la base de datos Scopus, para el período enero de 1979 - abril de 2021, permitió observar 636 documentos indexados. Los resultados mostraron un aumento en el número de documentos por año en las últimas dos décadas. Además, los países con mayor producción fueron China, Estados Unidos y Alemania según su número de publicaciones. Sin embargo, Estados Unidos, Canadá e Irán tuvieron el mayor impacto según la relación entre el número de documentos y el número de citas. Los principales determinantes fueron las condiciones económicas y de mercado, el clima, el funcionamiento del sistema eléctrico y la demanda de los clientes. Del mismo modo, la tendencia de investigación en los últimos años se enfoca en pronosticar el precio con mayor eficiencia.

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“…Benth et al [25] propose an original two-step estimation method: the estimation of the non-stationary factor by considering long-term FPDs (neglecting the effect of the short-term factor), and then the estimation of the CARMA factor by subtracting the estimated long-term factor from spot prices. More recently, Hinderks and Wagner [122] calibrate a model similar to that of Frestad et al [95] on German spot prices. The model that they consider is a two-factor model: a first factor driven by a NIG process and a second driven by a Brownian motion or a compound Poisson process.…”

Section: Normal Inverse Gaussian Processesmentioning

confidence: 99%

A survey of electricity spot and futures price models for risk management applications

Deschatre¹,

Féron²,

Gruet³

2021

Preprint

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This review presents the set of electricity price models proposed in the literature since the opening of power markets. We focus on price models applied to financial pricing and risk management. We classify these models according to their ability to represent the random behavior of prices and some of their characteristics. In particular, this classification helps users to choose among the most suitable models for their risk management problems.

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Factor models in the German electricity market: Stylized facts, seasonality, and calibration

Cited by 24 publications

References 37 publications

The Effect of Mean-Reverting Processes in the Pricing of Options in the Energy Market: An Arithmetic Approach

The Effect of Mean-Reverting Processes in the Pricing of Options in the Energy Market: An Arithmetic Approach

Determinantes del Precio de la Electricidad en Mercados No Regulados: Un Análisis Bibliométrico

A survey of electricity spot and futures price models for risk management applications

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