# Tag Info

## Hot answers tagged default

9

Distance to default $DD$ should be measured in standard deviations. You convert this into a probability $p_{default}$ using the normal CDF: $p_{default} = N(-DD)$. So if $DD = 2.978$ then the firm is about 3 standard deviations from default and has a $\frac{1 - 0.997}{2} = 0.0015 = 0.15 \%$ chance of defaulting in the next period. I divided by two because ...

6

In practice, I would begin with the recovery assumption. In the case of Greece, dealers are probably already quoting recovery swaps, allowing you to set this parameter directly. In general, you have to be willing to make assumptions based on history or on conversations with bankruptcy experts. Once I have the recovery assumption, I can take any instrument,...

3

I understand that Moody's uses an empirical distribution while KMV uses a normal distribution in order to calculate these probabilities KMV doesn't use a normal distribution to map distance to default to a probability of default (EDF in the KMV model). It uses a proprietary database. By a strict structural interpretation, $EDF$, the expected default ...

3

CDS provides protection against default. So when a firm is unable to pay the coupon (and there are few more scenarios where firms default) CDS is triggered. After default the liability holders have first claim on the firm's assets. If the assets are less than loan (say 60% of loan amount) then recovery can only be 60%. if these are risky assets and there ...

3

This is, of course, a very old play. The main thing that gets in the way of trading it is that puts are rarely available in a quantity that matches typical credit instrument notionals. Here's a decent paper by Peter Carr on the topic, see equation (4) and surrounding.

3

I think the national regulators are more concerned with downturn LGD (sort of TTC LGD) rather than a TTC PD. Therefore most rating systems which I encounter are closer to being PIT and thereby easier to validate using the techniques you mentioned and also to backtest. But in any case, model validation is a very subjective field despite the various ...

2

I have an Idea perhaps it helps you a bit (even though it deviates somewhat from your original setup). Let's assume you know the "anaffected" default probabilities for each bank $P(X_1<=C_1), \dots, P(X_n<=C_n)$. (Here I assumed that bank $i$ defaults when it's value falls below a certain value $C_i$) Now e.g. for bank $n$ you can calulate \$P(X_1<=...

1

To Recap: Your "Note" is a pool a of loans of which are expected to pay Yield Ydf. You want to estimate the mean and variance of the Loss in yield of non payment. First and foremost you need to get a historical YL or at least a Data Generating Process for YL. Some approaches A) Historical Calculate historically implied loss in yield and then use that ...

1

A methodology for estimating rating/ region/ sector proxies for ACVA calculations can be found here: http://www.nomura.com/resources/europe/pdfs/cva-cross-section.pdf Please let me know if you need anything to be clarified (caveat: I am one of the authors). The methodology assigns a CDS mark to counterparties that either have no CDS marks, or their marks are ...

1

Your reference says "This method derives implied CDS spreads for unobservable issuers through the interpolation or extrapolation of observable CDS. It is a factor model that constructs CDS spread surface as a function of credit rating and maturity." So this is for issuers which do not have any CDS contracts priced (there are no CDS spreads to bootstrap). I'...

1

There are quite a few methods to calculate default probabilities from CDS data. Simply you start at the shortest tenor, assume constant hazard rate. Then for the next tenor, you assume the previous hazard rate is still valid till the previous tenor, and the hazard rate between the previous tenor and new tenor is calibrated so that CDS PV matches the market ...

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