Electricity Futures Prices: Indirect Storability, Expectations, and Risk Premiums

Huisman, Ronald; Kilic, Mehtap

doi:10.1016/j.eneco.2012.04.008

Cited by 44 publications

(48 citation statements)

References 17 publications

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“…(Ψ(e −iω )ΣΨ (e +iω )) jj (5) It is crucial to note that (f(ω)) jk represents the contribution of the k-th variable to the portion of the spectrum of the j-th variable at a given frequency ω. To find the generalized decomposition of variance to different frequencies, (f(ω)) jk can be weighted by the frequency share of the variance of the j-th variable.…”

Section: Spillover Resultsmentioning

confidence: 99%

“…For this reason, electricity futures trading is more extensive than spot trading. There are also many studies concerning the electricity futures market (e.g., [5][6][7][8]).…”

Section: Introductionmentioning

confidence: 99%

“…According to the BP Statistical Review of World Energy 2019 [9], in 2018, approximately 40.5% of electricity was produced by oil, gas, and coal sources in Europe, which also necessarily implies the possibility of the spillover effects across the fossil fuel markets and electricity market. Additionally, in Huisman and Kilic's [5] study, they state that although electricity cannot be stored directly, it can be stored in the sense that fossil fuels are storable. The electricity producer who wants to sell an electricity futures contract can either purchase the amount of fossil fuels in the spot market and store it study of the spillover effect with a fixed investment horizon in a quantitative way.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Fluctuations in Fossil Fuel Commodities on Electricity Markets: Evidence from Spot and Futures Markets in Europe

Liu

Nakajima

et al. 2020

Energies

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Using a fresh empirical approach to time-frequency domain frameworks, this study analyzes the return and volatility spillovers from fossil fuel markets (coal, natural gas, and crude oil) to electricity spot and futures markets in Europe. In the time domain, by an approach developed by Diebold and Yilmaz (2012) which can analyze the directional spillover effect across different markets, we find natural gas has the highest return spillover effect on electricity markets followed by coal and oil. We also find that return spillovers increase with the length of the delivery period of electricity futures. In the frequency domain, using the methodology proposed by Barunik and Krehlik (2018) that can decompose the spillover effect into different frequency bands, we find most of the return spillovers from fossil fuels to electricity are produced in the short term while most of the volatility spillovers are generated in the long term. Additionally, dynamic return spillovers have patterns corresponding to the use of natural gas for electricity generation, while volatility spillovers are sensitive to extreme financial events.

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Section: Spillover Resultsmentioning

confidence: 99%

“…For this reason, electricity futures trading is more extensive than spot trading. There are also many studies concerning the electricity futures market (e.g., [5][6][7][8]).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Fluctuations in Fossil Fuel Commodities on Electricity Markets: Evidence from Spot and Futures Markets in Europe

Liu

Nakajima

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…While this is a potentially promising approach, it has to be taken into account that forward prices include risk premia, which should somehow be separated from the spot price forecast for it to be useful. And this is not an easy task since risk premia vary over time (Botterud et al, 2010;Huisman and Kilic, 2012;. There are also some discouraging examples.…”

Section: Discussionmentioning

confidence: 99%

Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

2013

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When building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data. While the short-term seasonal components (daily, weekly) are more regular and less important for valuation of typical power derivatives, the long-term seasonal components (LTSC; seasonal, annual) are much more difficult to tackle. Surprisingly, in many academic papers dealing with electricity spot price modeling the importance of the seasonal decomposition is neglected and the problem of forecasting it is not considered. With this paper we want to fill the gap and present a thorough study on estimation and forecasting of the LTSC of electricity spot prices. We consider a battery of models based on Fourier or wavelet decomposition combined with linear or exponential decay. We find that all considered wavelet-based models are significantly better in terms of forecasting spot prices up to a year ahead than all considered sine-based models. This result questions the validity and usefulness of stochastic models of spot electricity prices built on sinusoidal long-term seasonal components.

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“…However, the risk premium behaves differently for electricity than for other commodities, mainly because its inherent nonstorability invalidates the usual cash-and-carry relationship. These characteristics are further discussed in Fleten and Lemming (2003); Botterud et al (2010); Huisman and Kilic (2012). In terms of electricity derivative markets, Longstaff and Wang (2004) find that there are significant risk premia in the short-term forward prices in the Pennsylvania, New Jersey, and Maryland electricity market, confirmed by Kolos and Ronn (2008) in the same market for long-term forward prices.…”

Section: Literature Reviewmentioning

confidence: 84%

Selective hedging in hydro-based electricity companies

2013

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We analyze risk management trends in electricity commodity markets using the production and transaction data and written hedging policies of 12 Norwegian hydropower companies. The scope of our analysis is the hedging of physical electricity production using the power derivatives available at NASDAQ OMX Commodities. In their hedging policy, these companies either use a Cashflow at Risk (C-FaR) approach or a hedge ratio approach, or follow no explicitly stated approach. We find that the derivative cashflows constitute substantial profits for these companies. Furthermore, hedging contributes to reducing the C-FaR for 10 of the companies. These findings are surprising considering that we expect hedging to yield zero expected profit and to smooth the earnings function. Overall, our findings reveal that a practice of incorporating market views in hedging decisions is widespread in the sample companies, as both sanctioned in their written hedging policy and as indicated by the substantial hedging profits.

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Electricity Futures Prices: Indirect Storability, Expectations, and Risk Premiums

Cited by 44 publications

References 17 publications

Influence of Fluctuations in Fossil Fuel Commodities on Electricity Markets: Evidence from Spot and Futures Markets in Europe

Influence of Fluctuations in Fossil Fuel Commodities on Electricity Markets: Evidence from Spot and Futures Markets in Europe

Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

Selective hedging in hydro-based electricity companies

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