External rating scales are designed to convey information about either a specific instrument, called an issue-specific credit rating, or information about the entity that issued the instrument, which is called an issuer credit rating, or both. The ratings are typically one-dimensional, and the ratings scale goes from the highest rating to the lowest in uniform increments.
The ratings process usually consists of the following steps:
Researchers have composed tables (known as a transition matrices) that show the frequency of default, as a percent, over given time horizons for bonds that began the time horizon with a given rating. These tables use historical data to report that for bonds that began a 3-year period with an Aa rating, for example, a certain percent defaulted during the five years. These tables demonstrate that the higher the credit rating, the lower the default frequency.
Previous internal credit ratings approaches were very simplistic, as they often just identified a company as being either a good or a bad borrower. This process lacked the ability to assign unique interest rates based on individual probability of default (PD) and loss given default (LGD).
Two key factors have contributed to the increase in sophistication of internal credit ratings: the growing use of external credit rating agency language in the financial markets and the encouragement of Basel II rules to refine the approach for calculating credit risk capital requirements. Internal credit ratings models continue to improve, but key issues still exist regarding objectivity, data quality, time horizon, and consistency with external ratings.
Ratings agencies determine the external rating of a firm or bond using current information with the goal of indicating the probability of future events such as default and/or loss. The probability of default given any rating at the beginning of a cycle increases with the horizon. The increase in the default rates, or cumulative default rate, is much more dramatic for non-investment grade bonds. In addition to the condition of the firm, forecasted events in the horizon will affect the probabilities. The most notable events are the economic and industrial cycles. Since the rating should apply to a long horizon, in many cases, ratings agencies try to give a rating that incorporates the effect of an average cycle. This practice leads to the ratings being relatively stable over
Ratings agencies apply their ratings to different types of firms around the world, and the ratings may be interpreted differently given a specific industry and geographic location.
IMPACT ON BOND:
A rating downgrade is likely to make the bond price decrease (stronger evidence).
A rating upgrade is likely to make the bond price increase (weaker evidence).
IMPACT ON STOCK PRICES:
For stocks, the change in bond ratings has an even more asymmetric effect on the stock prices than it does on bond prices:
A rating downgrade is likely to lead to a stock price decrease (moderate evidence).
A rating upgrade is somewhat likely to lead to a stock price increase (evidence is mixed).
Internal ratings can have a procyclical effect on the economy since banks often change ratings with a lag with respect to the change in the economy. Thus, after the economic trough has been reached, it is possible that a bank may downgrade a company poised for recovery with the use of additional credit from the bank.
In order to build an internal rating system, banks should create ratings that resemble those set by ratings agencies. However, before banks can link default probabilities to internal ratings, it is necessary to backtest the current internal rating system.
An internal rating system may be biased by several factors. The following list identifies the main factors:
1.Time horizon bias: mixing ratings from different approaches to score a company
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