Selection of Value at Risk Models for Energy Commodities

By Alessandro G. Laporta, Luca Merlo, Lea Petrella. Energy Economics, 2018

71 citations recorded by the source corpus.

In this paper we investigate different VaR forecasts for daily energy commodities returns using GARCH, EGARCH, GJR-GARCH, Generalized Autoregressive Score (GAS) and the Conditional Autoregressive Value at Risk (CAViaR) models. We further develop a Dynamic Quantile Regression (DQR) one where the parameters evolve over time following a first order stochastic process. The models considered are selected employing the Model Confidence Set procedure of Hansen et al. (2011) which provides a superior set of models by testing the null hypothesis of Equal Predictive Ability. Successively information coming from each model is pooled together using a weighted average approach. The empirical analysis is conducted on seven energy commodities. The results show that the quantile approach i.e. the CAViaR and the DQR outperform all the others for all the series considered and that, generally, VaR aggregation yields better results.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6d35be211e0b8e25a453a240120464ca46660e0a

Citations in the corpus

• Does COVID-19 open a Pandora's box of changing the connectedness in energy commodities? (2020, unknown): “With the considerable trade volume and financial hedge demand, these commodities are always treated as a suitable basket of series for analyzing the financial characteristics of the energy market and understanding the internal or external cross-market linkages (Wang and Wu, 2012; Laporta et al., 201”