Credit Risk Management Analysis
Credit risk emanates from the peril of loss of principal or shortfall of a financial reward stemming from a borrower default to repay a loan obligation. The failures to honor a contractual agreement of a loan results of a wholesale credit risk. When a borrower envisages to use future cash flow to pay a current debt credit risk will eventually arise. Then the savers will assume credit risk by way of interest payment from the borrower or issuer of the debt obligation.
The wholesale credit risk, therefore, emanates from the defaulters of debt obligation that have been acquired from commercial banks (Berger, and Udell, 2006 n.p). Credit risk may also be experienced in many other financial institutions that engage with their client through bills of exchange. In the event of a default of a debt obligation, then the issuing intuition has to contend with the loss which may result in bankruptcy. The source of credit risk is mainly defaulters of debt obligation.
Most empirical studies, including this study venture to understand the structure of credit risk spread in various models in relationship to the bankruptcy process. The research establishes the relationship between the Marko chains in credit rating. An assessment of credit risk to models, states that it is easy to establish the loss distribution of a credit portfolio using credit rating (Altman, and Sabato, 2006 p. 716). Markey exposure is none existence in credit risks situations because market risk and credit migration are in the model.
Management of credit risk
The management of the credit risk requires deliberate efforts to manage the exposure. The exposure should be addressed by targeting markets, credit portfolio mix, and prudential exposure ceiling, price, and concentration limit and non-price terms (Arslan, 2009 p. 367). Other important aspects to consider in the process of mitigating the loss that arises from credit risk include establishing structures limits through the approval authorities. If the alternatives of credit risk affect the reporting system, provisioning norms and prudential accounting then they form an exception to this procedure.
The credit policy and procedure are the guiding principles in assessing the credit exposure of any firm. The credit risk is evaluated by the different business units and the many credit standards which prescribe the credit policy. The credit exposure sources are then analyzed through a thorough subjugation and a great deal of risk analysis based on product type and customer profiles. The whole process should be conducted by a specialist and professionals in the credit and Market risk department.
The wholesale credit risk management process requires that each borrower be graded according to the financial health of the SME. The rating scale varies from 1 to 10. 1 indicating the best while 10 indicates the worst credit score for any individual credit exposure. The model used in the process of analyzing the credit risk exposure uses different variables. The variables used have a correlation to the wholesale credit department of intermediaries (Berg, 2007, n.p).
The risks that the study considers include industry risk, business risk, management risk and financial risk. The inputs in the risk categories that have been mentioned above are combined to provide arithmetic rating. The arithmetic rating is a function of cumulative weighted tally based on the estimation under the four categories of risk. Credit exposure is tied to the potential exposure from counterparties, balance sheet or off-balance sheet items. The credit approval process is based on three internal systems, including approval and the variation process.
The relevant accounting-based model
The credit default model has the ability to separate defaulting and the non-defaulting small and medium firms. The models also have the capacity to predict bankruptcy of a firm and then classify them into two units. The first one is the market-based model and the second one is the accounting based model. The majority of firms belong the Market-based model because of their structure and their reduced form of approach. The model also utilizes information that is obtained in the capital market to inform decision of default. The small firms that will not have the capacity to post their information to the public through the capital markets have another option. The Smaller firms can utilize the accounting variable from their financial statements. The accounting method is the center of this research and it is used in the assessment of the credit worthiness of the firms.
Accounting-based models can be applied to smaller firms to determine their credit worthiness. The relevant regulatory framework usually is a Basel II regulatory framework which resulted in many developing models specifically for SME. The models have contributed to around 89% of the successful credit default of the same SMEs hence making them popular with a different financial institution. However, the SMEs play a crucial role in the development of the economy to both the so called developed and those countries in the bracket of developing. Altman and Sabato gave their analysis in the year 2010 concerning the SMEs in the UK for the period ranging 1994-2002 and stress the importance of using paradigm designed to assess the rating of the SMEs. They contrasted it to the popular belief of using one size fit’s all mentality of estimating default.
Altman and Sabato, (2006, 723) also developed different models that were relevant for the SMEs as contrasted to the large corporations. The research suggested that it is important to separate the small or medium enterprises from the larger enterprises because larger firms may lead banks to lower the required capital as required by Basel II. In His later work, Altman et al suggested that using the explanatory variables, including legal action by the creditor to recover the defaulted unpaid debts were informed by the power of risk models.
Multivariate Discriminate Analysis Model
This study employsMultivariate Discriminate Analysismodel utilizing a multiple variable in the analysis of the many of the credit default of UK SMEs. The study employs single variable for analysis of the likelihood of default. The study utilizes multiple variables for the purpose of prediction of the credit risk as well as the credit worthiness of the different firms. The biggest drawback with this paradigm is that it doesn’t present a big picture rather it provides information about the narrower nature (Becchetti, and Sierra, 2002 p. 91). The foundation of the study was to highlight firms that had defaulted on their obligation over a period of time (Altman, 1968, 590). The study utilizes a number of ratios to determine the credit risk that the SMEs posed to the financial sector in the UK. The ratios that the Univariate model use are
Cash flow to total debt ratio
Net income to total assets ratio
Total debt to total assets ratio
Working capital to total assets ratio
The result of the study indicates that the ratios obtained from failed firms declined over time. The declines were a clear indication of failure on the part of the firms. However, the firms had a higher predictive capability with success rates of over 87% accuracy.
Estimating the predictive capacity of the model
The predictive accuracy of the model can be obtained by using the Mean Squared errors MSE to find the type 1 and 2 errors. The about 100 firms are selected to form a sample in which 10 are active and 90 are inactive. The hold-out sample is a test to see if the model is accurate enough to conclusively signal of credit risk. If the model does not give the signal of credit risk then it is pure noise and does not prove the same.
With a 95% confidence level, we can conclusively say that the 87% of all the SMEs in the UK is non-defaulters on their loan obligation. Only 13% of the SMEs are defaulters on their loan obligation. Utilizing the Type 1 and 2 errors to analyze the model we discover that about 20% of our estimation was not accurate. The indication that the majority of the SMEs were within the sample indicate the model is accurate (Calabrese, and Osmetti, 2013 p.131). The hold-out sample indicates that 80% of all observation was within the sample.
The observation suggests that the accounting-based model has a superior predictive performance. The model can identify defaulting SMEs than that of any other traditional alternatives (Banasik, and Crook, 2007 n.p). The models also identified defaulters playing a crucial role for most financial intermediaries such as the banks. The model helps banks to identify defaulters (BCBS 2005 p.165). Commercial banks can utilize the model to identify defaulting SMEs hence mitigate their credit risk exposure.
Using the Basel IRB method to calculate the capital requirement for all the SMEs. The equity amount that each SME should contribute to the company is around 79% of the total business (Calabres, 2013, 30). The value is arrived at based on the model’s predictive capacity. If the capital deployed is little then the firm is bound to be bankrupt or at least they will default on their financial obligation (Calabrese, Marra, and Osmetti, 2013 n.p). The IRB method facilitates the advancement of the business model and the financial stability of it operation.
Conclusion and Recommendation
The model has proved and confirmed that the model’s main advantage lies in the accuracy in forecasting defaulting SMEs. The model forecasted on different SMEs through a different default horizon. The other advantage of the model is the relaxation of the linearity assumption which slightly differs with our data and conclusion. Our model has demonstrated the capacity to assist commercial banks and many other financial institutions identify defaulted SMEs. The model has also shed light on the kind of relationship between SMEs and response. Additionally, the model has the capacity to explore any individual SME’s likelihood of bankruptcy. The accuracy of the model is achieved because it is able to decompose multiple variables simultaneously.
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