Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/274606 
Year of Publication: 
2023
Series/Report no.: 
Working Paper No. 7/2023
Publisher: 
Örebro University School of Business, Örebro
Abstract: 
This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative Bayesian estimation approach called the density-tempered sequential Monte Carlo method. Our findings indicate that the inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV-type models. Notably, GAS-family models exhibit superior performance in terms of in-sample fit, out-of-sample forecast accuracy, as well as Value-at-Risk and Expected Shortfall prediction.
Subjects: 
ES
GARCH
GAS
log marginal likelihood
MIDAS
SV
VaR
JEL: 
C22
C52
C58
G32
Document Type: 
Working Paper

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