Pricing Options on Forwards in Energy Markets: The Role of Mean Reversion's Speed

Schmeck, Maren Diane

doi:10.2139/ssrn.2730691

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…This is an observed feature of prices volatilities, common to many commodity markets, consisting of increasing volatility of prices as time approaches maturity [27]. For an analysis of the impact of the Samuelson effect on option pricing, see [33] and [11].…”

Section: A Two-factor Model Based On Normal Inverse Gaussian Processesmentioning

confidence: 98%

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Capturing the power options smile by an additive two-factor model for overlapping futures prices

Piccirilli¹,

Schmeck²,

Vargiolu³

2019

Preprint

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In this paper we introduce an additive two-factor model for electricity futures prices based on Normal Inverse Gaussian Lévy processes, that fulfills a no-overlapping-arbitrage (NOA) condition. We compute European option prices by Fourier transform methods, introduce a specific calibration procedure that takes into account no-arbitrage constraints and fit the model to power option settlement prices of the European Energy Exchange (EEX). We show that our model is able to reproduce the different levels and shapes of the implied volatility (IV) profiles displayed by options with a variety of delivery periods.

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Section: A Two-factor Model Based On Normal Inverse Gaussian Processesmentioning

confidence: 98%

“…seasonality, stochastic volatility and Samuelson-like effects in pricing of power options (e.g. [11,12,26,31,32,33,37]) or commodity options (e.g. [3,4,34,35]).…”

Section: Introductionmentioning

confidence: 99%

Capturing the power options smile by an additive two-factor model for overlapping futures prices

Piccirilli¹,

Schmeck²,

Vargiolu³

2019

Preprint

Self Cite

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“…This general approach of the "convenience yield" type is also strongly questioned in empirical studies on energy commodities as, for example, in [60], and is naturally questioned in the context of electricity which is not storable. The following papers [127,177,154] propose, as initiated in [180], the rewriting of the models in a more general way by talking about unobservable stochastic factors:…”

Section: Gaussian Factors Modelsmentioning

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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“…An easy way to acknowledge its existence is to use futures price dynamics with a delivery time that represents the midpoint of the delivery period. This approach has been followed, for example, by Schmeck (2016), and is advantageous as it captures the typically observed behavior that the futures prices do not converge against the electricity spot price if time approaches the beginning of the delivery. A possible way to model the delivery period explicitly is to average the spot price or an artificial futures price over the entire delivery time.…”

Section: Introductionmentioning

confidence: 99%

“…Schneider and Tavin (2018) include such a term-structure effect within the framework of stochastic volatility modeling. Schmeck (2016) investigates analytically the impact of the Samuelson effect on option pricing.…”

Section: Introductionmentioning

confidence: 99%

The Market Price of Risk for Delivery Periods: Pricing Swaps and Options in Electricity Markets

Annika¹,

Schmeck²,

Kh.³

2020

Preprint

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In electricity markets, futures contracts typically function as a swap since they deliver the underlying over a period of time. In this paper, we introduce a market price for the delivery periods of electricity swaps, thereby opening an arbitrage-free pricing framework for derivatives based on these contracts. Furthermore, we use a weighted geometric averaging of an artificial geometric futures price over the corresponding delivery period. Without any need for approximations, this averaging results in geometric swap price dynamics. Our framework allows for including typical features as the Samuelson effect, seasonalities, and stochastic volatility. In particular, we investigate the pricing procedures for electricity swaps and options in line with Arismendi et al. ( 2016), Schneider andTavin (2018), andFanelli andSchmeck (2019). A numerical study highlights the differences between these models depending on the delivery period. JEL CLASSIFICATION: G130 • Q400 KEYWORDS: Electricity Swaps • Delivery Period • Market Price of Delivery Risk • Seasonality • Samuelson Effect • Stochastic Volatility • Option Pricing • Heston Model * Financial support by the German Research Foundation (DFG) through the Collaborative Research Centre 'Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications' is gratefully acknowledged.

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Pricing Options on Forwards in Energy Markets: The Role of Mean Reversion's Speed

Cited by 3 publications

References 14 publications

Capturing the power options smile by an additive two-factor model for overlapping futures prices

Capturing the power options smile by an additive two-factor model for overlapping futures prices

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

The Market Price of Risk for Delivery Periods: Pricing Swaps and Options in Electricity Markets

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