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.

Selection of Value at Risk Models for Energy Commodities

Merlo, Luca;
2018-01-01

Abstract

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.
2018
Value at Risk
model selection
energy comodities
File in questo prodotto:
File Dimensione Formato  
Petrella_Selection-Value-Risk-Models_2018.pdf

non disponibili

Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 642.41 kB
Formato Adobe PDF
642.41 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14092/3589
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact