
Explanation:
Statement I is correct: Extreme Value Theory (EVT) can be applied to unknown distributions as long as they are well-behaved. Statement II is correct: If the shape parameter () of the Generalized Extreme Value (GEV) distribution goes to zero, the distribution converges to the Gumbel distribution. Statement III is incorrect: If the tail parameter is less than zero, the GEV converges to the Weibull distribution, not the normal distribution. Statement IV is incorrect: As the threshold value increases, the distribution of exceedances converges to a Generalized Pareto Distribution (GPD), not a generalized Gumbel distribution.
Thus, statements III and IV are incorrect.
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Q.30 Salim Abdalla, FRM, is the CRO at an Iranian bank. For some time now, the bank has used internal models generally governed by the central limit theorem, but Abdalla is worried the existing models may not be addressing the possibility of random extreme losses that could create a major destabilizing effect. In a bid to stir change, he recommends the use of extreme value theory. While discussing EVT with one of his colleagues, the following statements are made:
I. To apply the EVT, the underlying loss distribution can be any of the commonly used distributions, e.g. normal or lognormal, and will usually be unknown
II. If the tail parameter of the generalized extreme value (GEV) distribution goes to zero, then the GEV essentially becomes a Gumbel distribution
III. If the tail parameter of the generalized extreme value (GEV) distribution is less than zero, the GEV becomes the normal distribution
IV. As the threshold value is increased, the distribution of exceedances converges to a generalized Gumbel distribution
Which of the statements above are incorrect?
A
I and III
B
II and IV
C
III and IV
D
I and II
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