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

Deschatre, Thomas; Féron, Olivier; Gruet, Pierre

doi:10.1016/j.eneco.2021.105504

Cited by 17 publications

(9 citation statements)

References 130 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Possible answers to this question are provided by Cont and Tankov 10(chap. 9,13) which propose both Bayesian methods based on an appropriate choice of the prior distributions and regularized non-linear-least-squares using the relative entropy or Kullback-Leiber distance to select the best risk-neutral measure. On the other hand, this inquiry goes beyond the scope of this work and we do not add further details.…”

Section: An Option Pricing Formula Under the Vg++ Modelmentioning

confidence: 99%

“…Example of applications of such models to energy markets can be found, among others, in Frestad et al, 7 which considers NIG‐laws to model the forward Nordic market, whereas Benth et al 8 offers a wider overview on stochastic modeling applied to the energy sector. Finally, a recent and exhaustive survey of more than thirty years of electricity spot and futures prices modeling can be found in Deschatre et al 9 …”

Section: Introductionmentioning

confidence: 99%

“…Finally, a recent and exhaustive survey of more than thirty years of electricity spot and futures prices modeling can be found in Deschatre et al. 9 However, some real data exhibit characteristic periods of constant values especially in illiquid markets like some not-so-mature energy markets. In such cases, adopting the financial interpretation that the subordinating process represents the trading activity, the gamma process (and the other subordinators mentioned above) imply that in any finite time-interval the number of trades cannot be zero because its trajectory is strictly increasing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Variance Gamma++ process and applications to energy markets

Gardini

Sabino

Sasso

2022

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

The purpose of this article is to introduce a new Lévy process, termed the Variance Gamma++ process, to model the dynamics of assets in illiquid markets. Such a process has the mathematical tractability of the Variance Gamma process and is obtained by applying the self-decomposability of the gamma law.Compared to the Variance Gamma model, it has an additional parameter representing the measure of the trading activity. We give a full characterization of the Variance Gamma++ process in terms of its characteristic triplet, characteristic function, and transition density. In addition, we provide efficient path simulation algorithms, both forward and backward in time. We also obtain an efficient "integral-free" explicit pricing formula for European options. These results are instrumental to apply Fourier-based option pricing and maximum likelihood techniques for the parameter estimation. Finally, we apply our model to illiquid markets, namely to the calibration of European power futures market data. We accordingly evaluate exotic derivatives using the Monte Carlo method and compare these values to those obtained using the Variance Gamma process and give an economic interpretation. Finally, we illustrate an extension to the multivariate framework.

show abstract

Section: An Option Pricing Formula Under the Vg++ Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Variance Gamma++ process and applications to energy markets

Gardini

Sabino

Sasso

2022

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

show abstract

“…Then we observe increasing swings of prices: in the last 5% of the period, the price loses and gains back 2 € in a few minutes. As a comparison, Figure 2d provides real trajectories of intraday prices on EEX for three different hours of delivery, which are extracted from Deschatre and Gruet (2021) with the courtesy of the authors. We clearly observe the increase of the volatility price closer to maturity and the similarity between our model simulation (b) and the behavior of the intraday price (d).…”

Section: The Case With Jumpsmentioning

confidence: 99%

Equilibrium price in intraday electricity markets

Aïd

Cosso

Pham

2021

Mathematical Finance

View full text Add to dashboard Cite

We formulate an equilibrium model of intraday trading in electricity markets. Agents face balancing constraints between their customers consumption plus intraday sales and their production plus intraday purchases. They have continuously updated forecast of their customers consumption at maturity. Forecasts are prone to idiosyncratic noise and common noise (weather). Agents production capacities are subject to independent random outages, which are each modeled by a Markov chain. The equilibrium price is defined as the price that minimizes trading cost plus imbalance cost of each agent and satisfies the usual market clearing condition. Existence and uniqueness of the equilibrium are proved, and we show that the equilibrium price and the optimal trading strategies are martingales. The main economic insights are the following: (i) when there is no uncertainty on generation, it is shown that the market price is a convex combination of forecasted marginal cost of each agent, with deterministic weights. Furthermore, the equilibrium market price is consistent with Almgren and Chriss's model, and we identify the fundamental part and the permanent market impact. It turns out that heterogeneity across agents is a necessary condition for Samuelson's effect to hold. We show that when heterogeneity lies only on costs, Samuelson's effect holds true. A similar result stands when heterogeneity lies only on market access quality. (ii) When there is production uncertainty only, we provide an approximation of the equilibrium for large number of players. The resulting price exhibits increasing volatility with time.

show abstract

“…For pricing, most of the models proposed in the literature focus on the design of stochastic models, among which we can cite the famous Schwartz [1997] or Schwartz and Smith [2000]. We refer to Deschatre et al [2021] for a comprehensive survey of these stochastic commodity models. The design of these models is expensive, and once a model is available it remains to tackle the tedious task of its calibration.…”

Section: Introductionmentioning

confidence: 99%

Deep Generators on Commodity Markets; application to Deep Hedging

Boursin¹,

Remlinger²,

Mikael³

et al. 2022

Preprint

View full text Add to dashboard Cite

Driven by the good results obtained in computer vision, deep generative methods for time series have been the subject of particular attention in recent years, particularly from the financial industry. In this article, we focus on commodity markets and test four state-of-the-art generative methods, namely Time Series Generative Adversarial Network (GAN) Yoon et al. [2019], Causal Optimal Transport GAN Xu et al. [2020], Signature GAN Ni et al. [2020] and the conditional Euler generator Remlinger et al. [2021], are adapted and tested on commodity time series. A first series of experiments deals with the joint generation of historical time series on commodities. A second set deals with deep hedging of commodity options trained on the generated time series. This use case illustrates a purely data-driven approach to risk hedging.

show abstract

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

Cited by 17 publications

References 130 publications

The Variance Gamma++ process and applications to energy markets

The Variance Gamma++ process and applications to energy markets

Equilibrium price in intraday electricity markets

Deep Generators on Commodity Markets; application to Deep Hedging

Contact Info

Product

Resources

About