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9

Fitch should be available right here: Sovereign Ratings History With Moody's it's not so easy, I don't know if there's a complete source available free of charge. But Sovereign Default and Recovery Rates, 1983-2007 has some data in the appendix III, though not so up to date and in a not that convenient format. Same goes about S&P, Sovereign Ratings ...


5

You cannot do it. It is an under-determined problem. That is to say, a whole multitude (subspace of $\mathbb{R}^{N\times N}$) of migration matrices will agree with any given table of default probabilities. Say you want to find a transition matrix for 2 states (IG, HY) plus default $$\left(\begin{matrix} p_{11} & p_{12} & p_{1D} \\ p_{21} ...


5

S&P credit rating change information until 2012 (European Union only): http://www.standardandpoors.com/ratings/articles/en/us/?articleType=HTML&assetID=1245327302187


4

(P) prefix : As a service to the market and typically at the request of an issuer, Moody's will assign a provisional rating when it is highly likely that the rating will become final after all documents are received, or an obligation is issued into the market. A provisional rating is denoted by placing a (P) in front of the rating. Such ratings may also be ...


4

Actually, there is a practical way to do it. You can use you PoD estimates to assign a credit rating to your securities and then use a published transition matrix for your purposes. Or you can estimate transition probabilities by linear interpolation based on the PoD values that you have. Here is a publication containing transition matrices from ...


3

Most of the papers concern CDS spreads which you will need to convert to a PD. Paper using country specific fundamentals: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2517018 This paper uses leverage: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2361872 Another one that decomposes them against peer groups: ...


3

This is an interesting question. I'll make a guess on what may be the driving factors for "ratings inflation" based on these assumptions: Rating agencies compete among themselves to conduct bond rating business with issuers, since they are paid for their services by the issuer. Bond issuers choose the agency that promises the highest rating, since the ...


3

If the transition matrix has distinct eigenvalues, you can diagonalize it and then take the cube root of the diagonal. E.g., you can compute the SVD, verify that the eigenvalues are distinct, take the cube root of the diagonal matrix, then re-multiply it together.


3

Yes, you can have two different ratings. The issuer has one credit rating, but the individual issues, even if they are both senior unsecured/secured with the same maturity, coupon, etc. can have different ratings. The key factor is going to be the structure/provisions of the issue itself. For example, an issue with a sinking fund is going to be viewed as ...


3

You are right, the rules to time-scale a T-years transition matrix $M_T$ are: $M_{k·T} = M_T^k$ $M_{T/k} = \sqrt[k]{M_T}$ The root of a matrix M can be obtained using the spectral decomposition: $M = P·D·P^{-1} \Longrightarrow M^k = P·D^k·P^{-1}$ where $P$ and $D$ are the eigenvectors and eigenvalue matrices of $M_T$. Note: The Perron-Frobenius tells ...


2

Depending upon how much data you have, you might find Violi (2004) useful. Nickell et al. (2000), while principally considering time-dependent stability tests, refers a bit to significance testing between the matrices of different agencies and might also provide some insight.


2

I am also not aware of any papers in this area. But having developed many such models, I can list the important steps: Decide on the target variable: usual choices are historical default data, agency ratings and expert rankings Create a sample containing the possible predictors Reduce the list with the help of some expert, e.g. exclude all the predictors ...


2

You can do this using the optim function in R. One possible solution is as follows: base <- c(0.9190, 0.0739, 0.0072, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0113, 0.9126, 0.0709, 0.0031, 0.0021, 0.0000, 0.0000, 0.0000, 0.0010, 0.0256, 0.9119, 0.0533, 0.0062, 0.0021, 0.0000, 0.0000, 0.0000, 0.0021, 0.0536, 0.8794, ...


1

Firstly it's good to straighten out our goal. You correctly say, that IFRS9 requires analysis of expected losses. There are two components of expected losses. 1) Expected probability of a default event 2) Expected recovery rate So, not only do we need the probability but also the recovery rate. Luckily, both are approximated by the credit spread, which ...


1

Bloomberg has a Default Risk model, which is similar to what you are querying. You can see a screenshot in this PDF. There you can also see the kind of variables they use. You can access it by typing DRSK at the CDS screen is Bloomberg. (If the screenshot in the PDF is not clear enough, let me know and I can post one with better resolution from Bbg) This ...


1

I would think it is because it can be bound between 2 points it can assume wide range shapes It fits the data empirically (as you said) On a related note Sometime back I read a paper which might give you more formal reason. It is for estimating and simulating recovery rates . I havnt used it to model credit migration probabilities . But I think one ...


1

I believe that your problem can be formulated as: Find PD matrix that is as close as possible to a given PD matrix (result of some previous calibration, or the matrix computed using average hazard rate, or any other "target", or the penalty on non-smoothness) subject to the following constraints: The values that are given must be matched exactly ...


1

The typical approach is to try to fit a ratings migration matrix to available rating transition data. If default rates are all you have then that's going to be difficult. Instead, I might try to fit a separate reduced form credit model on survival probability $P_\ell$ for each rating $\ell$ by fitting the function $$ P_\ell(T) = \exp\left( -\int_0^T h(t) ...


1

Bloomberg terminal page: CSDR (="Sovereign Debt Ratings")


1

SD is selective default http://www.reuters.com/article/2013/06/28/us-cyprus-downgrade-standardandpoors-idUSBRE95R0YQ20130628 +u is unsolicited rating http://lexicon.ft.com/Term?term=unsolicited-rating


1

Reuters uses a proprietary model defined StarMine structural/SmartRatios Credit Risk model that has been developed by themselves and provided with the Reuters data service. It does not exist a formal definition or paper about the model, in which it is explained how to get that score; Reuters simply explains roughly what is in its website without going into ...



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