Post by Sapphire Capital on Aug 23, 2008 2:35:40 GMT 4
Credit Rating Migration: Quantifying Obligor Risk
Alysa V. Shcherbakova
Emory University, Department of Economics
August 9, 2008
Abstract:
This study focuses on estimating credit rating migration probabilities using a continuous record approach, which controls for the effects of idiosyncratic and systematic risk factors. Short- and long-run relationships between asset quality and obligor ratings are modeled and quantified using Moody's Default Risk Service (DRS) data on long-term bonds, rated during the 1970-2007 time period. The contribution to existing risk management literature is two-fold. First, we incorporate a continuous-record model, which allows us to mitigate problems of data sparsity and control for resulting estimation errors. Second, we introduce a methodology to precisely identify and control for stages of the business cycle, while allowing for heterogeneity in obligor characteristics, which enables us to identify the incremental impact of idiosyncratic and systematic risk factors on rating transitions probabilities, resulting in more precise estimates of credit rating migration trends. Results confirm that obligor characteristics and business cycle stages have a strong effect on the dynamics of credit ratings, with a stronger effect observed in longer-horizon models. We emphasize the importance of the business cycle effect on credit ratings by computing a forecast content for the business cycle variable in our model, further illustrating the importance of idiosyncratic risk factors to precise quantitative assessment of obligor risk.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1232562_code1085182.pdf?abstractid=1214062&mirid=2
Alysa V. Shcherbakova
Emory University, Department of Economics
August 9, 2008
Abstract:
This study focuses on estimating credit rating migration probabilities using a continuous record approach, which controls for the effects of idiosyncratic and systematic risk factors. Short- and long-run relationships between asset quality and obligor ratings are modeled and quantified using Moody's Default Risk Service (DRS) data on long-term bonds, rated during the 1970-2007 time period. The contribution to existing risk management literature is two-fold. First, we incorporate a continuous-record model, which allows us to mitigate problems of data sparsity and control for resulting estimation errors. Second, we introduce a methodology to precisely identify and control for stages of the business cycle, while allowing for heterogeneity in obligor characteristics, which enables us to identify the incremental impact of idiosyncratic and systematic risk factors on rating transitions probabilities, resulting in more precise estimates of credit rating migration trends. Results confirm that obligor characteristics and business cycle stages have a strong effect on the dynamics of credit ratings, with a stronger effect observed in longer-horizon models. We emphasize the importance of the business cycle effect on credit ratings by computing a forecast content for the business cycle variable in our model, further illustrating the importance of idiosyncratic risk factors to precise quantitative assessment of obligor risk.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1232562_code1085182.pdf?abstractid=1214062&mirid=2