Intraday power trading: toward an arms race in weather forecasting?

Kuppelwieser, Thomas; Wozabal, David

doi:10.1007/s00291-022-00698-5

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…The fact that other fundamental and trading related information, especially intraday forecast changes, do not yield additional predictive power suggests that this information is contained in the price already. These results support the notion of weak-form market efficiency already indicated by Narajewski and Ziel (2020a) and Kuppelwieser and Wozabal (2021).…”

Section: In-sample Analysis: Estimated Coefficients and Their Develop...supporting

confidence: 90%

“…Capturing this effect aims at improving the modelling of the location and volatility of ΔP. The findings will be discussed in the light of the market efficiency hypothesis indicated by Narajewski and Ziel (2020a); Kuppelwieser and Wozabal (2021). Additionally, the variance of individual forecast updates is used to model the volatility of ΔP.…”

Section: Introductionmentioning

confidence: 96%

“…The recent and still sparse literature on probabilistic forecasting in intraday markets (Janke and Steinke, 2019;Narajewski and Ziel, 2020a;Uniejewski et al, 2019;Cramer et al, 2022) and the markets' driving fundamentals has, so far, focused on modelling the impact of renewable forecast (Ziel, 2017;Kath, 2019;Pape et al, 2016;Gürtler and Paulsen, 2018;Balardy, 2022) and forecast errors (Ziel, 2017;Kulakov and Ziel, 2020;Kuppelwieser and Wozabal, 2021). A different strand of literature emerged around modelling of the merit-order effect for price changes and price elasticity (Kiesel and Paraschiv, 2017;Kremer et al, 2021Kremer et al, , 2020Kulakov and Ziel, 2019;Balardy, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

Hirsch¹,

Ziel²

2024

The Energy Journal

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During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe.

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Section: In-sample Analysis: Estimated Coefficients and Their Develop...supporting

confidence: 90%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

Hirsch¹,

Ziel²

2024

The Energy Journal

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Recurrent double-conditional factor model

Fieberg,

Liedtke,

Poddig

2024

OR Spectrum

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In economic applications, the behavior of objects (e.g., individuals, firms, or households) is often modeled as a function of microeconomic and/or macroeconomic conditions. While macroeconomic conditions are common to all objects and change only over time, microeconomic conditions are object-specific and thus vary both among objects and through time. The simultaneous modeling of microeconomic and macroeconomic conditions has proven to be extremely difficult for these applications due to the mismatch of dimensions, potential interactions, and the high number of parameters to estimate. By marrying recurrent neural networks with conditional factor models, we propose a new white-box machine learning method, the recurrent double-conditional factor model (RDCFM), which allows for the modeling of the simultaneous and combined influence of micro- and macroeconomic conditions while being parsimoniously parameterized. Due to the low degree of parameterization, the RDCFM generalizes well and estimation remains feasible even if the time-series and the cross-section are large. We demonstrate the suitability of our method using an application from the financial economics literature.

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