 Ask CARL: Forecasting tail probabilities for energy commoditiesBernardina Algieri and Arturo Leccadito Energy Economics, 2019, vol. 84, issue C Abstract: We use a set of conditional auto-regressive logit (CARL) models to predict tail probabilities for returns calculated from futures of four energy commodities. We show that CARL models are very useful to forecast the probability of tail events in energy markets and the forecasting ability of the models generally increases when commodity implied volatility is added as a predictor. We further present new bivariate models to jointly forecast the probabilities that returns from a given commodity and from the S&P 500 index are on the left tail and models for the coexceedances. We find that CARL family models have always a better forecasting performance than GARCH and Quantile-Augmented Volatility models in a univariate and multivariate setting. Conversely, when modelling coexceedances, CARL models exhibit a better predictive capacity only for Brent and heating oil. Keywords: Probability forecasting; Energy commodities; CARL models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319302786 DOI: 10.1016/j.eneco.2019.104497 Access Statistics for this article Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant More articles in Energy Economics from Elsevier |
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