Abstract
The Value at Risk (VaR) and the Conditional Value at Risk (CVaR) as measures that estimate risk, have been used in oil sector to measure extreme and unexpected scenarios of oil prices. Additionally, the Normal Inverse Gaussian (NIG) distribution, a special case of the Generalized Hyperbolic (GH) family, has been demonstrated to provide a better fit than Normal distribution to financial data. In this paper, we used NIG distribution to model a distribution of equity price returns in oil companies in Brazil, Russia, India and China (BRIC) economies in periods of unstable oil prices from 2004 to 2017, with the objective of demonstrating an underestimation of the risk measures when a Normal distribution is assumed and a more conservative estimate of those measures when considering a NIG distribution.
Highlights
As indicated by Baffes, Kose, Ohnsorge and Stocker (2015), in recent decades, the oil sector has reflected instability in international oil prices, which have been more representative since 2004 and respond to various factors, such as the policies of the Organization of Petroleum Exporting Countries, geopolitical tensions, imbalances between supply and demand in international markets and others
It has been shown that normality is not the best adjustment for financial instruments, resulting in better approximations of alternative distributions, as in the case of the gh family, proposed by Barndorff-Nielsen in 1977. This class of distributions is defined by five parameters; by fixing the parameter λ = -1⁄2, the nig distribution is obtained. He exposed the capability of nig distribution to model heavier tails than that of normal distribution, a fact commonly found in return data series (Barndorff-Nielsen, 1977, 1997)
This study provided evidence that in the equities of bric economy oil companies, the cvar model used with nig distribution provides the biggest estimate of potential losses, compared to the cvar estimate considering normal distribution or a var with nig or normal distribution
Summary
As indicated by Baffes, Kose, Ohnsorge and Stocker (2015), in recent decades, the oil sector has reflected instability in international oil prices, which have been more representative since 2004 and respond to various factors, such as the policies of the Organization of Petroleum Exporting Countries (opec), geopolitical tensions, imbalances between supply and demand in international markets and others. It has been shown that normality is not the best adjustment for financial instruments, resulting in better approximations of alternative distributions, as in the case of the gh family, proposed by Barndorff-Nielsen in 1977. This class of distributions is defined by five parameters; by fixing the parameter λ = -1⁄2, the nig distribution is obtained. He exposed the capability of nig distribution to model heavier tails than that of normal distribution, a fact commonly found in return data series (Barndorff-Nielsen, 1977, 1997)
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