Projecting oil prices under conditions of uncertainty has always been and will always remain a challenge. What makes this more of a challenge is the acceptance that the oil, and possibly other commodity and stock markets, do not behave in a random fashion generating normal distribution patterns.
As we will show, a partially manipulated/partially random model for oil prices incorporating uncertainty generates U shaped distributions. These distributions are difficult to model with a single best fitting distribution, but a segmented approach to obtaining several best-fitting distributions of the prescribed ranges will be shown to be a useful substitute.
This practical webinar will explore the subject of distribution fitting with @RISK and is based in a chapter of the book energy risk modeling by Roy Nersesian published by Palisade.