Electricity forward curves with thin granularity: Theory and empirical evidence in the hourly EPEXspot market

Caldana, Ruggero; Fusai, Gianluca; Roncoroni, Andréa

doi:10.1016/j.ejor.2017.02.016

Cited by 17 publications

(10 citation statements)

References 38 publications

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“…Their results provide an important input into the debate on whether retaining the flexibility to update emission targets is beneficial despite its negative effect of causing policy uncertainty. Similarly, Drake et al (2016) show that emissions price uncertainty under cap-andtrade policy results in greater expected profit than achieved under emissions tax policy with constant emissions price, which contradicts the conventional (2002), Lucia and Schwartz (2002), Fleten and Lemming (2003), Longstaff and Wang (2004), Bunn (2004), Carmona and Coulon (2014), Islyaev and Date (2015), and Caldana et al (2017) Pricing of electricity contracts and derivatives Kwon et al (2006), Thompson (2013), Islyaev and Date (2015), and Wu and Babich (2012) Electricity trading through forward and spot markets Sen et al (2006), Kwon et al (2006), Dong and Liu (2007) Sioshansi (2002), Eydeland and Wolyniec (2003), Deng and Oren (2006), and Liu et al (2006) Open research questions…”

Section: Climate Policy and Its Effect On The Electric Power Industrymentioning

confidence: 91%

Electric Power Industry: Operational and Public Policy Challenges and Opportunities

Parker

Tan

Kazan

2019

Production and Operations Management

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The electric power industry is undergoing dramatic change driven by rapid technological transformation, concern over climate change, and evolving market structures. As a result, there is a need for new models to help the industry better utilize resources in a time of increasing uncertainty and to help government policy makers better understand the impacts of their regulatory decisions. We provide a structured review of the operations research and management science literatures to describe the current operational and policy issues in the electric power industry, with a particular focus on issues surrounding electricity market design, renewable integration, effects of climate policy on electric power infrastructure, rise of electric powered vehicles, energy storage, and the growing interdependence between natural gas and electric power sectors. We identify the current research frontier and classify the existing research into clusters with respect to managerial issues addressed. We offer a forecast for where the electric power industry is going and describe some important public policy issues. Finally, we highlight research opportunities and discuss how the management science community can contribute by considering the interaction between operational considerations and electric power policy.

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Section: Climate Policy and Its Effect On The Electric Power Industrymentioning

confidence: 91%

Electric Power Industry: Operational and Public Policy Challenges and Opportunities

Parker

Tan

Kazan

2019

Production and Operations Management

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“…where d * is the last day in the calibration window and the single and double time indexes satisfy t = 24d + h. We consider two well-performing methods of extracting and modeling the LTSCone that is based on wavelet smoothing [2][3][4][5]7] and one on the Hodrick-Prescott (HP) filter [4,10,19,20]. In wavelet smoothing [1,35], the original time series is decomposed using the discrete wavelet transform into a sum of an approximation series capturing the general trend, S k , and a number of detail series, D k , representing higher-frequency components:…”

Section: Seasonal Decompositionmentioning

confidence: 99%

“…• Six-year-long electricity price and fundamental variables time series from two distinct power markets-Nord Pool and PJM Interconnection, providing two three-year-long test periods of hourly-resolution. • Two commonly used approaches for modeling the LTSC of electricity price seriesone based on wavelet smoothing [2][3][4][5]7] and one on the Hodrick-Prescott (HP) filter [4,10,19,20]. • A parameter-rich, LASSO-estimated autoregressive model with nearly 130 regressors, after [21] called the LEAR model.…”

Section: Introductionmentioning

confidence: 99%

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Jędrzejewski¹,

Marcjasz²,

Weron³

2021

Energies

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Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.

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“…Given historical information on day-ahead electricity prices and futures as well as other relevant information concerning the risk premia it is possible to construct and forecast the hourly price forward curve. This was done with real data for the German and Austrian electricity market for instance by Caldana et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures

Steinert¹,

Ziel²

2019

The Energy Journal

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Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the time series of electricity prices. This paper tackles this issue in a promising and novel approach. By combining the precision of econometric autoregressive models in the short-run with the expectations of market participants reflected in future prices for the short-and mid-run we show that the forecasting performance can be vastly increased while maintaining hourly precision. We investigate the day-ahead electricity price of the EPEX Spot for Germany and Austria and setup a model which incorporates the Phelix future of the EEX for Germany and Austria. The model can be considered as an AR24-X model with one distinct model for each hour of the day. We are able to show that future data contains relevant price information for future time periods of the day-ahead electricity price. We show that relying only on deterministic external regressors can provide stability for forecast horizons of multiple weeks. By implementing a fast and efficient lasso estimation approach we demonstrate that our model can outperform several other models in the literature.

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Electricity forward curves with thin granularity: Theory and empirical evidence in the hourly EPEXspot market

Cited by 17 publications

References 38 publications

Electric Power Industry: Operational and Public Policy Challenges and Opportunities

Electric Power Industry: Operational and Public Policy Challenges and Opportunities

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures

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