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In this paper, we study the methods of combining different volatility forecasts using various GARCH models. Given that the major risk exposure for many investors in energy is the volatility of the electricity price, our motivation stems from the fact that there is no single best model for...
Persistent link: https://www.econbiz.de/10012841582
correction model (VECM). Considering Italian data, the appropriate diagnostic tests and estimation results are in favour of non …
Persistent link: https://www.econbiz.de/10014193091
Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have...
Persistent link: https://www.econbiz.de/10014464238
Persistent link: https://www.econbiz.de/10009765836
In more deregulated markets such as the UK, demand forecasting is vital for the electric industry as it is used to set electricity generation and purchasing, establishing electricity prices, load switching and demand response. In this paper we produce improved short-term forecasts of the demand...
Persistent link: https://www.econbiz.de/10012964481
This paper sheds light on the questions whether it is possible to generate an accurate forecast of the real price of oil and how it can be improved using forecast combinations. For this reason, my work will investigate the out-of-sample performance of thirteen individual forecasting models. The...
Persistent link: https://www.econbiz.de/10012955548
, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter …
Persistent link: https://www.econbiz.de/10012544443
Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing...
Persistent link: https://www.econbiz.de/10012649104
We analyse the importance of macroeconomic information, such as industrial production index and oil price, for forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse...
Persistent link: https://www.econbiz.de/10011987142
In this paper we present an evaluation framework for predictions of binary events in probabilistic electricity price forecasting. It employs the MSE-equivalent QPS together with the DM test and allows for further insights about deficiencies of the considered models. Additionally, techniques from...
Persistent link: https://www.econbiz.de/10012133314