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Combination Forecasting of Energy Demand in the UK

Marco Barassi and Yuqian Zhao

Year: 2018
Volume: Volume 39
Number: Special Issue 1
DOI: 10.5547/01956574.39.SI1.mbar
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Abstract:
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 for energy produced from five different sources in the UK averaging from a set of 6 univariate and multivariate models. The forecasts are averaged using six different weighting functions including Simple Model Averaging (SMA), Granger-Ramanathan Model Averaging (GRMA), Bayesian Model Averaging (BMA), Smoothing Akaike (SAIC), Mallows Weights (MMA) and Jackknife (JMA). Our results show that model averaging gives always a lower Mean Square Forecast Error (MSFE) than the best/optimal models within each class however selected. For example, for Coal, Wind and Hydro generated Electricity forecasts generated with model averaging, we report a MSFE about 12% lower than that obtained using the best selected individual models. Among these, the best individual forecasting models are the Non-Linear Artificial Neural Networks and the Vector Autoregression and that models selected by the Jackknife have often superior performance. However, MMA averaged forecasts almost always beat the predictions obtained from any of the individual models however selected, and those generated by other model averaging techniques.



Cryptocurrency Bubble on the Systemic Risk in Global Energy Companies

Qiang Ji, Ronald D. Ripple, Dayong Zhang, and Yuqian Zhao

Year: 2022
Volume: Volume 43
Number: Special issue
DOI: 10.5547/01956574.43.SI1.qiji
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Abstract:
Financialization has brought new challenges to the international energy markets, making energy systemic risk a more complicated issue. One of the important features is the development of cryptocurrency, which has become a critical part of the global financial markets. As a consequence, the rise and fall of cryptocurrency can have nonnegligible impacts on the systemic risks in the international energy sector. This paper empirically tests this hypothesis using the equity data of the top 100 energy companies from 2014 to 2021. Specifically, we explore the extreme shocks of cryptocurrency using multiple bubble tests, and then we test to what extent bubbles in cryptocurrency markets can affect systemic risk in the energy sector. Our empirical results show that the formation of cryptocurrency bubbles, especially when the bubbles burst, significantly increases systemic risks in the energy sector. This effect retains the same in the recent COVID-19 pandemic period. In addition, oil and gas companies play an essential channel in the risk spillover from cryptocurrency markets to the international energy markets.





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