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Mazzeu, J. H. G., Veiga, H. & Mariti, M. B. (2019). Modeling and forecasting the oil volatility index. Journal of Forecasting. 38 (8), 773-787
Export Reference (IEEE) 
J. H. Mazzeu et al.,  "Modeling and forecasting the oil volatility index", in Journal of Forecasting, vol. 38, no. 8, pp. 773-787, 2019
Export BibTeX 
@article{mazzeu2019_1716012369928,
	author = "Mazzeu, J. H. G. and Veiga, H. and Mariti, M. B.",
	title = "Modeling and forecasting the oil volatility index",
	journal = "Journal of Forecasting",
	year = "2019",
	volume = "38",
	number = "8",
	doi = "10.1002/for.2598",
	pages = "773-787",
	url = "https://onlinelibrary.wiley.com/doi/full/10.1002/for.2598"
}
Export RIS 
TY  - JOUR
TI  - Modeling and forecasting the oil volatility index
T2  - Journal of Forecasting
VL  - 38
IS  - 8
AU  - Mazzeu, J. H. G.
AU  - Veiga, H.
AU  - Mariti, M. B.
PY  - 2019
SP  - 773-787
SN  - 0277-6693
DO  - 10.1002/for.2598
UR  - https://onlinelibrary.wiley.com/doi/full/10.1002/for.2598
AB  - The increase in oil price volatility in recent years has raised the importance of forecasting it accurately for valuing and hedging investments. The paper models and forecasts the crude oil exchange-traded funds (ETF) volatility index, which has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. Analysis of the oil volatility index suggests that it presents features similar to those of the daily market volatility index, such as long memory, which is modeled using well-known heterogeneous autoregressive (HAR) specifications and new extensions that are based on net and scaled measures of oil price changes. The aim is to improve the forecasting performance of the traditional HAR models by including predictors that capture the impact of oil price changes on the economy. The performance of the new proposals and benchmarks is evaluated with the model confidence set (MCS) and the Generalized-AutoContouR (G-ACR) tests in terms of point forecasts and density forecasting, respectively. We find that including the leverage in the conditional mean or variance of the basic HAR model increases its predictive ability. Furthermore, when considering density forecasting, the best models are a conditional heteroskedastic HAR model that includes a scaled measure of oil price changes, and a HAR model with errors following an exponential generalized autoregressive conditional heteroskedasticity specification. In both cases, we consider a flexible distribution for the errors of the conditional heteroskedastic process.
ER  -