Document Type

Conference Proceeding

Publisher

The Modelling and Simulation Society of Australia and New Zealand Inc.

Faculty

Faculty of Business and Law

School

School of Business/Marketing and Services Research Centre

RAS ID

16982

Comments

This article was originally published as: Allen, D. E., Powell, R. , & Singh, A. (2013). A dynamic credit ratings model. In MODSIM2013, 20th International Congress on Modelling and Simulation (pp. 1291 - 1297). Canberra, Australia. The Modelling and Simulation Society of Australia and New Zealand Inc. Original article available here

Abstract

The Global Financial Crisis (GFC) provided overwhelming evidence of the problems caused by inadequate credit ratings. Losses and problem loans experienced by banks over this period were staggering. Yet many of the securitized sub-prime parcels which were widely seen as an underlying cause of the GFC, as well as corporate obligors who experienced severe difficulties during the GFC, retained extremely strong external credit ratings. They may have had low perceived risk at the time of rating, but as circumstances changed, the ratings stayed static and became far removed from the underlying risk. A key problem is that the external credit ratings do not fluctuate with changing economic circumstances. Whilst there are models which measure changing default risk, they are not linked to credit ratings and it is often the rating itself, not the underlying risk that drives behavior, such as the purchase of securitized parcels, the pricing of credit risk, and the allocation of capital for credit risk, which under the Basel standardized model for corporates is based on the rating itself. This problem is exacerbated by the fact that these ratings carry descriptors such as “extremely strong capacity”. This descriptor may no longer be appropriate for the rated company if the market turns dramatically, yet the rating and descriptor remain unchanged. To overcome this problem, this paper shows how an innovative fluctuating credit ratings model can be generated by linking the Merton structural credit model to a credit ratings framework. The Merton model measures fluctuations in daily asset values and, using a combination of these fluctuating asset values and the capital structure of a company, it measures Distance to Default (DD) and the Probability of Default (PD) associated with each DD. Under the Merton structural model, default occurs when the firm’s debt exceeds asset values. Thus as fluctuations in asset values become more volatile, DD also becomes more volatile and PD increases. External raters such as Moody’s provide PD’s associated with each rating. Thus by using the Merton model, we are able to generate PDs which fluctuate over time and link these PD’s to credit ratings. Therefore, as our PD’s fluctuate, so do the credit ratings. To illustrate our approach, we apply this model to a French motor vehicle company (Renault) which experienced severe distress during the GFC. We compare the Moody’s rating changes that took place for Renault over the 2006 – 2009 period, which captures the events leading up to and during the GFC. Over this period, only three Moody’s external ratings changes took place and throughout this period, Renault stayed in the Moody’s ‘moderate’ risk band. Based on this, an investor would likely assume the company was in reasonable financial health, and a bank would not be required to change its capital allocation for this company if it was a borrower. Yet during this period, the company experienced such severe financial problems that it had to be bailed out by the French Government. Our model, on the other hand, recognizes these stresses far quicker, starting with rating downgrades for Renault from August 2007 and moving downwards through several risk bands, from ‘moderate’ to ‘substantial’ to ‘high’ and then to ‘very high’ credit risk. This downward spiral is far more in keeping with the actual problems experienced by Renault than the static ‘moderate’ risk tag would indicate. We thus find that the new model responds extremely rapidly to changing economic circumstances to produce ratings which can far more accurately depict the underlying credit risk of a corporate obligor in these times than prevailing external rating methods. The new ratings can benefit bond investors and banks through improved knowledge of the underlying credit risk of bonds and of corporate borrowers. As capital adequacy can also be linked to credit ratings, an improved rating model can assist banks and regulators to better measure required capital adequacy to protect against economic downturns.

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