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on Forecasting |
| By: | Sheybanivaziri, Samaneh (Dept. of Business and Management Science, Norwegian School of Economics); Le Dréau, Jérôme (Laboratoire des Sciences de l’Ingénieur pour l’Environnement (LaSIE), La Rochelle University); Kazmi, Hussain (Dept. of Electrical Engineering, KU Leuven) |
| Abstract: | Due to the increase in renewable energy production and global socioeconomic turmoil, the volatility in electricity prices has considerably increased in recent years, leading to extreme positive and negative price spikes in many electricity markets. Forecasting (the risk of) these prices accurately in advance can enable risk-informed decision-making by both consumers and generators, as well as by the grid operators. In this work, focusing on day-ahead markets, we review recent developments in how price spikes are defined, as well as which explanatory factors and methodologies have been used to forecast them. The paper identifies seven categories of influencing factors, which come with over 30 sub-classifications that can cause price spikes. In terms of methodologies, probabilistic models are being increasingly utilized to capture uncertainty in the price forecast. The review uncovers a wide range in all of these choices as well as others, which makes it difficult to compare methods and select best practices for predicting price spikes. |
| Keywords: | Spikes; Electricity markets; Day-ahead market; Point forecast; Probabilistic forecasts |
| JEL: | C00 C10 C53 |
| Date: | 2024–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2024_001&r=for |
| By: | Dennis Koch Vahidin Jeleskovic; Zahid I. Younas |
| Abstract: | This paper introduces a unique and valuable research design aimed at analyzing Bitcoin price volatility. To achieve this, a range of models from the Markov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) model classes are considered and their out-of-sample forecasting performance is thoroughly examined. The paper provides insights into the rationale behind the recommendation for a two-stage estimation approach, emphasizing the separate estimation of coefficients in the mean and variance equations. The results presented in this paper indicate that Stochastic Volatility models, particularly SARV models, outperform MS-GARCH models in forecasting Bitcoin price volatility. Moreover, the study suggests that in certain situations, persistent simple GARCH models may even outperform Markov-Switching GARCH models in predicting the variance of Bitcoin log returns. These findings offer valuable guidance for risk management experts, highlighting the potential advantages of SARV models in managing and forecasting Bitcoin price volatility. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03393&r=for |