Electricity Forward Curves with Thin Granularity

Caldana, Ruggero; Fusai, Gianluca; Roncoroni, Andréa

doi:10.2139/ssrn.2777990

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…An alternative approach to extract power forward curves from a discrete set of traded contract using spline interpolation is suggested by Benth, Koekebakker, and Ollmar (2007). Recently, Caldana, Fusai, and Roncoroni (2016) proposed a method combining non-parametric filtering with convex interpolation.…”

Section: Generation Of Price Forward Curves: Theoretical Backgroundmentioning

confidence: 99%

A space-time random field model for electricity forward prices

Benth

Paraschiv

2018

Journal of Banking & Finance

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Stochastic models for forward electricity prices are of great relevance nowadays, given the major structural changes in the market due to the increase of renewable energy in the production mix. In this study, we derive a spatio-temporal dynamical model based on the Heath-Jarrow-Morton (HJM) approach under the Musiela parametrization, which ensures an arbitrage-free model for electricity forward prices. The model is fitted to a unique data set of historical price forward curves. As a particular feature of the model, we disentangle the temporal from spatial (maturity) effects on the dynamics of forward prices, and shed light on the statistical properties of risk premia, of the noise volatility term structure and of the spatio-temporal noise correlation structures. We find that the short-term risk premia oscillates around zero, but becomes negative in the long run. We identify the Samuelson effect in the volatility term structure and volatility bumps, explained by market fundamentals. Furthermore we find evidence for coloured noise and correlated residuals, which we model by a Hilbert space-valued normal inverse Gaussian Lévy process with a suitable covariance functional.

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Section: Generation Of Price Forward Curves: Theoretical Backgroundmentioning

confidence: 99%

A space-time random field model for electricity forward prices

Benth

Paraschiv

2018

Journal of Banking & Finance

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“…However, they need a separate step to solve the arbitrage question and their methodology focuses on the hourly price forward curve. Also Caldana et al (2016) considered some notions of robustness in their proposed model. As a downside, the outlier detection rule they propose uses estimated standard deviations which are highly susceptible to outliers on their turn as well.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, they propose a dynamic semiparametric factor model. On the other hand, Caldana, Fusai, and Roncoroni (2016) noted that the algorithm of Borak and Weron (2008) suffers from underfitting market prices and may fail to account for short-term periodical patterns. A comparison between various approaches including works by Fleten and Lemming (2003), Benth et al (2007), and Paraschiv, Bunn, and Westgaard (2016) may be found in Kiesel et al (2018).…”

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confidence: 99%

Multivariate constrained robust M‐regression for shaping forward curves in electricity markets

Leoni

Segaert

Serneels

et al. 2018

Journal of Futures Markets

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In this paper, a multivariate constrained robust M‐regression method is developed to estimate shaping coefficients for electricity forward prices. An important benefit of the new method is that model arbitrage can be ruled out at an elementary level, as all shaping coefficients are treated simultaneously. Moreover, the new method is robust to outliers, such that the provided results are stable and not sensitive to isolated sparks or dips in the market. An efficient algorithm is presented to estimate all shaping coefficients at a low computational cost. To illustrate its good performance, the method is applied to German electricity prices.

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A Space-Time Random Field Model for Electricity Forward Prices

Benth

Paraschiv

2016

SSRN Journal

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Electricity Forward Curves with Thin Granularity

Cited by 3 publications

References 35 publications

A space-time random field model for electricity forward prices

A space-time random field model for electricity forward prices

Multivariate constrained robust M‐regression for shaping forward curves in electricity markets

A Space-Time Random Field Model for Electricity Forward Prices

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