Probability of default

 


Diese Unterscheidung spielt insbesondere bei Institutionelle Anlegern wie Pensionskassen oder Versicherungen eine wichtige Rolle, da diese oftmals per Gesetz oder durch ihre eigenen Statuten verpflichtet sind, nur Anleihen von Schuldnern zu kaufen, die ein bestimmtes Mindestrating haben.

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The greater accuracy of PIT PDs makes them the preferred choice in such current, risk applications as pricing or portfolio management. After that, one transforms these factors into convenient units and expressed them as deviations from their respective, long-run-average values. At this point, one has a set of indices measuring the distance between current and long-run-average DD in each of a selected set of sectors.

To obtain PIT PDs, one introduces the relevant indices into the relevant default models, re-calibrate the models to defaults, and apply the models with current and projected changes in indices as inputs.

The specific model formulation depends on the features important to each, distinguished class of counterparties and data constraints. Some common approaches include:. There are many alternatives for estimating the probability of default. Default probabilities may be estimated from a historical data base of actual defaults using modern techniques like logistic regression.

Default probabilities may also be estimated from the observable prices of credit default swaps , bonds , and options on common stock. For small business default probability estimation, logistic regression is again the most common technique for estimating the drivers of default for a small business based on a historical data base of defaults.

These models are both developed internally and supplied by third parties. A similar approach is taken to retail default, using the term " credit score " as a euphemism for the default probability which is the true focus of the lender. Some of the popular statistical methods which have been used to model probability of default are listed below.

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Columbia Business Law Review. This is known as 'Blanco' LGD. Different types of statistical methods can be used to do this. Gross LGD is most popular amongst academics because of its simplicity and because academics only have access to bond market data, where collateral values often are unknown, uncalculated or irrelevant. Blanco LGD is popular amongst some practitioners banks because banks often have many secured facilities, and banks would like to decompose their losses between losses on unsecured portions and losses on secured portions due to depreciation of collateral quality.

The latter calculation is also a subtle requirement of Basel II , but most banks are not sophisticated enough at this time to make those types of calculations. To determine required capital for a bank or financial institution under Basel II, the institution has to calculate risk-weighted assets. This requires estimating the LGD for each corporate, sovereign and bank exposure. There are two approaches for deriving this estimate: However, under certain special circumstances the supervisors, i.

Under the A-IRB approach and for the retail-portfolio under the F-IRB approach, the bank itself determines the appropriate loss given default to be applied to each exposure, on the basis of robust data and analysis. The analysis must be capable of being validated both internally and by supervisors. Thus, a bank using internal loss given default estimates for capital purposes might be able to differentiate loss given default values on the basis of a wider set of transaction characteristics e.

These values would be expected to represent a conservative view of long-run averages. A bank wishing to use its own estimates of LGD will need to demonstrate to its supervisor that it can meet additional minimum requirements pertinent to the integrity and reliability of these estimates. It can be mortgages or it can be a custody account or a commodity.

The higher the value of the security the lower the LGD and thus the potential loss the bank or insurance faces in the case of a default. For example, as of , there were nine companies in the United Kingdom with their own mortgage LGD models. In Switzerland there were two banks as of In the corporate asset class many German banks still only use the values given by the regulator under the F-IRB approach.

Under Basel II, banks and other financial institutions are recommended to calculate 'Downturn LGD' downturn loss given default , which reflects the losses occurring during a 'Downturn' in a business cycle for regulatory purposes. One definition is at least two consecutive quarters of negative growth in real GDP. Although there are several advanced Aaa-rated OECD countries with lower debt ratios and better fiscal outlooks than the US, their markets are generally too small to play that role.

Which is a pretty silly idea. Sovereign defaults are always political, rather than economic: Silver goes on to complain that credit ratings are a lagging indicator: Again, this is a complaint only if you think of the ratings agencies as being some kind of guide to help people beat the market.

Silver is right when he says this means that a country which has been downgraded to AA is a worse bet than a country that has been upgraded to AA: But the ratings agencies are very good at emphasizing that two countries with the same credit rating are far from identical in other respects.