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Fits distill information to a few parameters. Parameters are then used to gauge risk and compare data sets. The parameters obtained from the fit in figure 5 provide a better data representation than the figure 4 fit parameters.
Figure 5. Percent change histogram with improved fit. Red curve plots the more complex fit. Green curve plots the 95% upper confidence limit. Blue curve plots the 95% upper prediction limit.
Financial models relying on symmetrical distributions (i.e., Gaussian/Normal, or other symmetric distributions) exhibit what is termed "skewness risk". Kurtosis risk ("fat tail" risk) is another area where underlying model assumptions influence risk calculations. Both skewness and kurtosis risk can have important implications for risk measurement (i.e., value at risk (VAR)).
Oil prices are positively biased (but only slightly). Obviously, based on figure 1, prices rise with time and must have net positive bias. Positive fluctuations are ~3% more likely than negative price fluctuations. This value is close to current world GDP of ~4% (International Monetary Fund (IMF) estimate).
Given this knowledge, long and short-term strategies accounting for random and systematic risk exist. Such strategies may net significant financial gain.
Raw data source: US Department of Energy
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