I'm currently facing the problem of how properly (analytically) adjust the parameters of an aggregated Vasicek (2002) loss distribution so that it has the same expected loss and 99% quantile as the sum of the standalone credits (i.e. no diversification effects).

Remember the formula of the asymptotic one-factor cumulative loss distribution, where $$P(L\le x) = F(x;p;\rho)=N\biggl(\frac{\sqrt{1-\rho} N^{-1}(x)-N^{-1}(p)}{\sqrt{\rho}}\biggr)$$

Also remember that $L$ is the fraction loss of the portfolio, i.e. $L \in [0;1]$, $\rho$ is the correlation of each loan with the risk factor (equicorrelation) and and $p$ is the probability of default. $\rho$ and $p$ is the same for all loans.

Now the problem is, that I have a loan portfolio of $n$ loans which have no equal correlation with the risk factor (because they belong to different sectors) and no equal probability of default (which is more realistic).

I now want to calibrate analytically the distribution above (find an aggregated $\rho$ and $p$) such that it holds the following:

$$\sum_{i=1}^n F_i(x;p_i;\rho_i ) = F(x;p;\rho)$$

Kind regards


You can first compute the average PD - few choices would be:

  1. Simple average of the individual PDs
  2. Exposure weighted average of the PDs
  3. If the PDs range is too large, then you might want to bucket them and apply the Vasicek formula to each bucket - this is how Basel approaches it.

Once you have the PD and LGD, then you can solve for the correlation. This is very much like finding the Black scholes implied vol - correlation serves the same purpose in the Vasicek. And if you want to research further then please look up base correlation: In the CDO tranches etc, the base correlation, is like the correlation in the Vasicek distribution, and pretty much carries the same meaning as the Black Scholes IV.

| improve this answer | |

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.