Credit risk grading has been for a long time been accredited to a number of financial crises in the world most recently the collapse of asset-backed commercial paper (ABCP) in 2006 and 2007 which was caused by massive defaults of mortgages which had been issued by various financial institutions in the US with very high rates. This collapse set out a chain of happenings to result in a very serious global financial crisis ever since the Great Depression in the 1930s. This has been one of the major negative impacts of credit risk to organizations. Improper management of credit risk has led to falling of major financial institutions in the world e.g. Lehman Brothers.
In this essay credit risk and Credit Risk Grading (CRG) will be discussed and the importance of CRG to an organization will be spelled out.
CRG is a very crucial activity in which the organization needs to include in their risk management processes. We all know that no type of risk can be completely eliminated but organizations should continually pursue ways in which they can mitigate its impacts in case the unexpected happens.
Credit Risk
This is the possibility that a counterparty or borrower will not meet the agreed-upon obligations. More than 50% of the entire risk elements in banks and financial institutions worldwide are Credit risk alone. Managing credit risk in attaining efficient management of financial institutions has increasingly become an important task. Management of credit risk is about identifying, measuring, matching mitigations, monitoring and controlling of the exposures of credit risk (Nickell, et al 1998).
Credit risk mostly does not happen in isolation. For instance, an interest rate rise can be detrimental to the creditworthiness of the issuer of the bond hence increasing the credit risk of the financial institution holding the bonds. On the other hand, a fall in the bond value increases the market risk for the financial institution.
What we don’t know
Credit Risk Grading
Credit risk grading (CRG) refers to a pre-specified scale which reflects the credit risk which is underlying for a given level of exposure. Credit risk uses employs the use of alphabet/symbol/number which represents the risks associated with a certain credit exposure. CRG is the appropriate module for establishing a system of credit risk management.
Credit Risk Grading is a crucial element that helps in the process of credit risk management as it helps the various financial institutions in understanding the dimensions of risk that come along when dealing with credit transactions. This task of credit risk grading of lines of business, borrowers and business activities provides a better view of the credit portfolio quality of a financial institution. Credit risk grading systems are important in taking appropriate decisions at both post and pre-sanction stages (Carey, 1998).
Credit grading helps at the pre-sanction stage to sanction authority in deciding on whether to lend or not, the extent of exposure, what the lending price should be, the various facilities, appropriate credit facility, and the numerous risk mitigation techniques to put a cap on the level of risk (Lopez, 1999a).
CRG can be beneficial at the post-sanction stage in helping financial institutions decide about the depths of the renewal or review, the frequency of the review, frequency of the grading and several other measures to be taken. CRG should be invoked at the start of lending and it should be updated annually. Grading of credit risk should, however, be reviewed in instances of adverse events. Portfolio monitoring entails that financial institutions should come up with reports concerning credit risk exposure by risk grade. Enough migration and trend analysis should be done to come up with any credit facility deterioration. Accuracy and consistency of the grading of the credit risk should be periodically examined by a function, for instance, an independent credit review.
What we should know
Functions of Credit Risk Grading
CRG systems that are well managed to promote soundness and safety in financial institutions through enhancing decision making which is informed. Credit risk is measured by grading systems which also differentiates groups and individual credits by the risks they offer. CRG allows management and examiners of financial institutions to observe trends and changes in their levels of risk. CRG further allows the management of financial institutions to efficiently manage risk and optimize their results (Kupiec, 1995).
Use of Credit Risk Grading
In summary
In taking lessons from the sub-prime mortgage lending crisis also commonly known as ‘mortgage meltdown’ or ‘mortgage mess’ that started in the US mortgage market, financial institutions can now understand better the concept of credit risk that existed in the sub-prime borrowing by individuals and lending by financial institutions.
After considering the importance of credit risk grading, it is, therefore, a noble idea for players in the financial system to develop carefully a model for grading credit risk which is in line with the objective outlined above (Berkowitz, 1999).
References
Altman, E.& Saunders, A., (1997). “Credit Risk Measurement: Developments over the Last Twenty Years,” Journal of Banking and Finance, 21, 1721-1742.
Basle Committee on Banking Supervision, (1999). “Credit Risk Modelling: Current Practices & Applications,” Basle Committee on Banking Supervision, Basle. (http://www.bis.org/press/index.htm)
Berkowitz, J., (1999). “Evaluating the Forecasts of Risk Models,” Manuscript, Trading Risk Analysis Group, Federal Reserve Board of Governors.
Carey, M., (1998). “Credit Risk in Private Debt Portfolios,” Journal of Finance, 53, 1363-1388.
Diebold, F.X., Gunther, T. & Tay, S., (1997). “Evaluating Density Forecasts with Applications to Financial Risk Management,” International Economic Review, 39, 863-883.
Diebold, F.X., Hahn, J. &Tay, A.S., (1998). “Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange,” Manuscript, Department of Economics, University of Pennsylvania.
Diebold, F.X. and Lopez, J.A., (1996). “Forecast Evaluation and Combination,” in Maddala, G.S., and Rao, C.R., eds., Handbook of Statistics, Volume 14: Statistical Methods in Finance, 241- 268. Amsterdam: North-Holland.
Diebold, F.X. and Mariano, R., (1995). “Comparing Predictive Accuracy,” Journal of Business and Economic Statistics, 13, 253-264.
Kupiec, P., (1995). “Techniques for Verifying the Accuracy of Risk Measurement Models,” Journal of Derivatives, 3, 73-84.
Lopez, J.A., (1999a). “Regulatory Evaluation of Value-at-Risk Models,” Journal of Risk, forthcoming.
Lopez, A., (1999b). “Methods for Evaluating Value-at-Risk Estimates,” Federal Reserve Bank of San Francisco Economic Review, forthcoming.
Nickell, P., Perraudin, W. & Varotto, S., (1998). “Ratings- Versus Equity-Based Credit Risk
Modeling: An Empirical Analysis.” Manuscript, Conference on Credit Risk Modelling & Regulatory Implications.
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