|
Basel II |
|
Bank for International Settlements |
| Background |
| Pillar 1: Regulatory Capital |
|
Credit risk |
| Pillar 2: Supervisory Review |
| Pillar 3: Market Disclosure |
| Business and Economics Portal |
Probability of default (PD) is the likelihood of a default over a particular time horizon. It provides an estimate of the likelihood that a client of a financial institution will be unable to meet its debt obligations[1][2]. PD is a key parameter used in the calculation of economic capital or regulatory capital under Basel II for a banking institution.
Contents |
The probability of default (also call Expected default frequency) is the likelihood that a loan will not be repaid and will fall into default. PD is calculated for each client who has a loan (for wholesale banking) or for a portfolio of clients with similar attributes (for retail banking). The credit history of the counterparty / portfolio and nature of the investment are taken into account to calculate the PD.
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. The simplest approach, taken by many banks, is to use external ratings agencies such as Standard and Poors, Fitch or Moody's Investors Service for estimating PDs from historical default experience. 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.
The following steps are commonly used:
Through-the-Cycle (TTC) PD's are long-run probabilities of default which take into consideration upturns and downturns in the economy. Conceptually, it is the simple average, median or equilibrium of Point-In-Time (PIT) PD's (PD's which are calculated for very short horizons) over a long period of time where several economic cycles have played out. Usually, the simple regulatory formula is to take the long-term average of PIT PD's. This is, however, impractical as long-term data is often limited for any obligor/portfolio making calculations cumbersome. Furthermore, it is theoretically incorrect as obligor/portfolio characteristics tend to metamorphisize over time making one estimation of PD at one point-in-time incomparable with another estimate at another point-in-time.
In order to overcome these practical and theoretical hurdles it is possible to convert pure PIT estimated PD's to TTC or Long-Term PD's by following some simple steps:
As most Practitioners have little data on PD's compared to data on losses, this method provides a way of overcoming practical challenges. Furthermore, the method takes into consideration existing default definitions (and changing default definitions) and cyclical effects caused by macroeconomic forces as represented in Loss Frequency Data. One crucial assumption, however, is the belief that the segment/obligor type has remained relatively constant over the time period the time-series has been created for.
Expected Default Frequency (EDFTM) is a trademarked term for the probability of default derived from Moody's Analytics' (formerly Moody's KMV) public firm model, a structural credit risk model originally based on the work of Stephen Kealhofer, John McQuown, and Oldrich Vasicek. The public firm EDFTM model reflects numerous theoretical and empirical variations on the traditional Black-Scholes-Merton structural model, and is probably the best-known commercial implementation of the structural modeling framework. Although often associated with a one-year time horizon, a term structure of EDFTM credit measures from one to five years is calculated.
In June 2011 Moody's Analytics introduced Through-the-Cycle EDF credit measures to the market. Through-the-Cycle EDF (TTC EDF) credit measures are probabilities of default that are largely free of the effect of the aggregate credit cycle, primarily reflecting a firm’s enduring, long-run credit risk trend. TTC EDF measures are derived from Moody’s Analytics’ public firm EDF model through a filtering technique that separates the underlying components of EDF measures that correspond to the observed frequency of the credit cycle. According to the Moody's Analytics' TTC EDF model, the key difference between point-in-time and through-the-cycle credit measures lies in their information content: a PIT measure incorporates all relevant credit trend information in estimating a firm’s creditworthiness, whereas a TTC measure moves primarily in response to a firm’s underlying, long-run credit quality trend, tuning out changes attributed to cyclical variation that is likely to be reversed with the passage of time.