Very helpful @David Harper CFA FRM
Strange how other sources employ approach resulting in significantly different results.
From what i was able to read so far this is my synopsis viz. the above workout:
(1,3)(3,1): Discordant cuz 1<3 BUT 3>1
(1,4)(2,3): Concordant cuz 1<4 AND 2<3
Thanks a bunch @David Harper CFA FRM...but please bear with me...i am not able to follow through...
Please provide an alternate view...maybe with examples:
Also, if we look at earlier definition on "neither" where xt=xt* or yt=yt*...say (1,4)(2,4) as in...
Dear @David Harper CFA FRM
Knowing default is characterized by a bernoulli distribution, can you please advise if an analytical solution exists to deriving PDs from sigma PD.
Let me be more precise..if sigma PD = 7%. What is PD? Would appreciate if you share the workout!
Hi @David Harper CFA FRM
Please confirm if we are required perform calculations of the below for FRM 2 May 2017 exam:
1- VAR and ES under POT
2- WCDR and Maturity Adjustment under Basel IRB approach.
There seems to be inconsistency on treatment of pairs neither concordant nor discordant.
My understanding is any pair (x,y) where x=y fits into the "neither" category.
Can someone or @David Harper CFA FRM please confirm.
Thanks @David Harper CFA FRM
Helpful, but a bit of sherlock holmes re. your last statement!
Wouldn't a distribution with mean 0 and variance of 1 by default be a standard normal distribution, hence 1.65 deviate to 95% VaR quantile?!!
I think the key is for the distribution to be elleyptical...
A 95% VaR measure that assumes normal distribution cuts off at 1.65 critical z.
If an alternative distribution entails a 95% VaR at 1.56, what does that tell us about properties of the distribution?
Is is safe to assume it exhibits thinner tails?