Chapter 18: Copulas Flashcards

1
Q

The properties of a copula IOV

A
  1. Increasing function of each of the individual variables in it
  2. One CDF not 1 but the rest are then the copula should be equal to the CDF of the remaining variable
  3. Valid probability value produced for any valid combination of variables
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2
Q

Axioms for a good measure of concordance CUIS CCC

A

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

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3
Q

Merits of an explicit copula CITE SA

A

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

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4
Q

Applications of Gumbel copula UC

A

o Only upper tail dependence – no lower tail

o Credit loss portfolio modelling

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5
Q

Application of a Frank copula NJE

A

o No tail dependency or symmetric form
o Joint survivor modelling
o Exchange rate movement modelling

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6
Q

Application of Clayton copula LUI

A

o Lower tail dependency is possible
o Upper tail dependency not there
o Investment returns on a portfolio

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7
Q

Merits of Gaussian copula CATS

A
  • 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
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8
Q

Benefits of a copula JEI

A
  • Joint distribution does not have to be modelled
  • Explicit relationship between interrelated factors shown
  • Invariance - marginal distribution can be independently adjusted
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9
Q

Types of copulas FIE

A
  • Fundamental – copulas showing the basic dependencies that variables can display
  • Implicit
  • Explicit – copulas expressed as closed form functions
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10
Q

Choosing and fitting a copula CUP MP

A

• 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)

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