rahul.goyl
Member
Hi David,
what is the right answer, why
Extreme value theory (EVT) can assist with value at risk (VAR) calculations by providing better probability estimates of observing extreme losses than that indicated by a standard normal distribution because empirical distributions exhibit fat tails. If one uses the generalized Pareto distribution (GPD) method to generate parameter estimates for the shape parameter, fat tails will indicate a:
A)positive parameter estimate and VAR calculations that are too large.
B)positive parameter estimate and VAR calculations that are too small.
C)negative parameter estimate and VAR calculations that are too small.
D)negative parameter estimate and VAR calculations that are too large.
Regards,
Rahul
what is the right answer, why
Extreme value theory (EVT) can assist with value at risk (VAR) calculations by providing better probability estimates of observing extreme losses than that indicated by a standard normal distribution because empirical distributions exhibit fat tails. If one uses the generalized Pareto distribution (GPD) method to generate parameter estimates for the shape parameter, fat tails will indicate a:
A)positive parameter estimate and VAR calculations that are too large.
B)positive parameter estimate and VAR calculations that are too small.
C)negative parameter estimate and VAR calculations that are too small.
D)negative parameter estimate and VAR calculations that are too large.
Regards,
Rahul
