Education
- Overview
- 01 Introduction to ERM
- 02 External risk frameworks
- 03 The ERM process
- 04 Risk classification
- 05 Risk measurement
- 06 Introduction to risk modelling
- 07 Quantitative analysis of financial data
- 08 Further risk modelling
- 09 Analysis of different types of risk
- 10 Risk optimisation and responses to risk
- 11 Risk Mitigation
- 12 Capital Management
Further Risk Modelling
These questions cover tail risk and extreme value theory
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Question 1 of 5
1. Question
Match each of the following copulas to the appropriate tail dependence description
Sort elements
- Upper tail dependence
- No tail dependence
- Lower tail dependence
- Upper and lower tail dependence
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Gumbel
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Frank
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Clayton
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t
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Question 2 of 5
2. Question
Match each of the following ranges for the generalised extreme value (GEV) distribution parameter g to the appropriate definition
Sort elements
- Power law tail (Fréchet)
- Exponential tail (Gumbel)
- Finite tail (Weibull)
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g>0
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g=0
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g<0
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Question 3 of 5
3. Question
If modelling an exteme event where the tail of the distribution is fatter than would be seen under the normal (Gaussian) distribution, in which range should the generalised extreme value (GEV)
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Question 4 of 5
4. Question
If using the generalised extreme value (GEV) distribution to model risks, what is the name of the approach that models the highest observation in each block of data?
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Question 5 of 5
5. Question
When fitting a generalised Pareto distribution, what is the name of te function used to help determine the threshold to the tail of the distribution?
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