EconPapers    
Economics at your fingertips  
 

Large Time-Varying Volatility Models for Electricity Prices

Angelica Gianfreda (), Francesco Ravazzolo () and Luca Rossini

No No 05/2020, Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School

Abstract: We study the importance of time-varying volatility in modelling hourly electricity prices when fundamental drivers are included in the estimation. This allows us to contribute to the literature of large Bayesian VARs by using well-known time series models in a huge dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exploit the importance of both Gaussian and non-Gaussian error terms in stochastic volatility. We find that by using regressors as fuels prices, forecasted demand and forecasted renewable energy is essential in order to properly capture the volatility of these prices. Moreover, we show that the time-varying volatility models outperform the constant volatility models in both the in-sample model- fit and the out-of-sample forecasting performance.

Keywords: Electricity; Hourly Prices; Renewable Energy Sources; Non-Gaussian; Stochastic-Volatility; Forecasting (search for similar items in EconPapers)
Pages: 28 pages
Date: 2020-07
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-ets, nep-for, nep-reg and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
https://hdl.handle.net/11250/2660739

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bny:wpaper:0088

Access Statistics for this paper

More papers in Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School Contact information at EDIRC.
Bibliographic data for series maintained by Helene Olsen ().

 
Page updated 2022-01-19
Handle: RePEc:bny:wpaper:0088