Chapter 18: Copulas Flashcards
The properties of a copula IOV
- Increasing function of each of the individual variables in it
- One CDF not 1 but the rest are then the copula should be equal to the CDF of the remaining variable
- Valid probability value produced for any valid combination of variables
Axioms for a good measure of concordance CUIS CCC
Completeness of domain: M defined for all value of X and Y
Unit range: M in (-1,1)
Independence: If so then M = 0
Symmetry: Mxy = Myx
Coherence: C1 > C2 then M1 > M2
Consistency: X = -Z then Mxy = -Mzy
Convergence: If c tends to C then m tends to M
Merits of an explicit copula CITE SA
o Closed form function
o Integration avoided – closed form functions
o Tail dependency of each copula assessed
o Easy to use
o Small number of parameters involved
o Application to Heterogeneous variables is limited
Applications of Gumbel copula UC
o Only upper tail dependence – no lower tail
o Credit loss portfolio modelling
Application of a Frank copula NJE
o No tail dependency or symmetric form
o Joint survivor modelling
o Exchange rate movement modelling
Application of Clayton copula LUI
o Lower tail dependency is possible
o Upper tail dependency not there
o Investment returns on a portfolio
Merits of Gaussian copula CATS
- Closed form function does not exist
- Associations from -1 to 1 allowed for, hence very comprehensive
- Tail dependency lacks
- Single parameter definition – correlation derived between variables
Benefits of a copula JEI
- Joint distribution does not have to be modelled
- Explicit relationship between interrelated factors shown
- Invariance - marginal distribution can be independently adjusted
Types of copulas FIE
- Fundamental – copulas showing the basic dependencies that variables can display
- Implicit
- Explicit – copulas expressed as closed form functions
Choosing and fitting a copula CUP MP
• Choosing
o Concordance
o Upper, lower tail lower tail dependencies
o Patterns of dependence
• Fitting
o MLE
o Parameterisation with rank correlation (Kendall’s tau)