Credit Based Strategies in Pricing Contracts and Assets

Financial institutions have used credit based strategies to evaluate credit worthiness of borrowers and counterparties for a long time. Despite their popularity, the strategies present various risks to financial institutions. Jorion and Zhang (2007) aver that banks and financial institutions collapse because of the lax standards in credit assessment. Many lenders have remained oblivious of the changing economic circumstances while pricing assets and contracts. This has led to great financial losses. Credit risk strategies seek to optimize financial institutions “risk-adjusted rate of return by maintaining credit risk exposure within acceptable parameters” (Thompson, 2010, p.4). This paper will evaluate the strengths and weaknesses of credit based strategies used in pricing contracts and assets.

Jorion and Zhang (2007) define credit risk as “the potential that a borrower or counterparty will fail to meet its obligations in accordance with agreed terms” (4). A financial institution, or any other lender, should endeavor to keep exposure to risk within manageable and acceptable parameters. There are various sources of credit risk for banks and other lending financial institutions. Bank loans are the most common. However, all activities within a bank constitute a sizeable degree of credit risk. Esposito (2011) cites newer sources of credit risk such as “financial futures, swaps, bonds, equities, options, and in the extension of commitments and guarantees, and the settlement of transactions” (5). Given the wide array of credit risks, it is important for stakeholders to create an enabling environment that would facilitate minimization of credit risks. In spite of the obvious risks involved, credit based strategies have several strengths in pricing contracts and assets.

If well managed, credit based strategies allow lenders to make sound credit decisions and uphold safety. Credit risk systems have evolved over time to develop elaborate rating systems that evaluate risk with high degree of accuracy. Lenders are now in a position to evaluate risk in a manner that will accrue optimal returns. Credit risk ratings have revolutionized credit risk management in several ways. The first one is in approving and underwriting credit worthiness. Before a lender gets into any contract with the borrower, there are mechanisms to evaluate with a high degree of accuracy whether the assets can cover all the loan expenses in case of inability to pay back.

The second strength lies in asset pricing. Credit based strategies allow the lender to correctly price a loan against the borrower’s assets. The strategies enable the lender to strike a balance between assets and loan without adverse effects on the financial institution. It has thus becomes easier to develop the right relationship between management and administration of credit. Credit ratings allow financial institutions to be more meticulous when analyzing a client’s creditworthiness. Credit specialists are at a better position to supervise the administration of loan facilities in a manner that brings optimal returns to the lender. In the recent times, specialists have been able to automate assessment of suitability to credit facilities. Interbank borrowings have particularly become easier because of synchronization of systems in many financial institutions.

Another credit based strategy in determining pricing of assets and contracts is credit risk models. The main strength of these models is that they enable financial institutions “measure the distributions of their potential credit losses at the top level of the institution” (Esposito, 2011, p.4). Institutions are therefore able to know how much capital to store to cushion against collapse in case of a big loss. The models have gained widespread acceptance especially by banks and Collateralized Debt Obligations. However, while guarding against defaulting, the models do not have calibration. This allows for default clustering. Jorion and Zhang (2007) point out that the second generations of credit risk models have mitigated the challenge of clustering though it is not entirely foolproof.

Proper staffing can improve the strength of credit risk models and reduce exposure to risk (Chan-Lau and Ong, 2007). Management of financial institutions must come up with effective measures to minimize exposure to credit risk. This is achievable through formulation of mechanisms to evaluate and monitor credit. These mechanisms will establish a credit culture that values accuracy in credit evaluation, reduces errors in credit rating, and assigns assistance to relevant departments to reduce credit risk exposure. Esposito (2011) argues that the best cover against credit risk exposure is a motivated and highly qualified staff. Credit rating skills should constitute an integral part of performance appraisal so that workers accord it the seriousness it deserves. An accurate and timely staff insures financial institutions against losses occasioned by poor credit risk assessment. However, a competent staff working with outdated systems and data will still be a risk to exposure. It is therefore important that financial institutions embrace dynamism and integrated credit risk ratings in order to complement efforts of the staff and provide the synergy to cover against credit risk exposure.

In spite of the strengths discussed above, credit based strategies have some weaknesses that increase credit risk exposure. Chan-Lau and Ong (2007) argue that while credit risk models have become complex over time, they are extremely difficult to calibrate. They attribute this to the fact that “correlations cannot be directly measured for specific obligors” (4). While traditional credit models lead to higher losses, the modern technical ones are leading to default clustering. Clustering leads to losses even when a financial institution is not a direct lender. As stated earlier, financial institutions store some capital to cushion against exposure to credit risk. With clustering, however, the capital saving may deplete when a financial institution suffers the ripple effect of lending money to a bank whose borrowers defaulted.

Another challenge of credit risk model strategies is counterparty risk. Cunat (2007) explains that this kind of risk occurs “when the default of on firm causes financial distress for its creditors” (4). One default triggers a plethora of other defaults, something that can cripple the operations of a financial institution. The fact that financial institutions are interrelated makes it easy for defaulting to cascade and spread from one financial institution to another. The challenge for the next generation of credit risk models is to ensure that exposure to counterparty risk remains relatively low if it is not possible to eliminate it completely.

Chan-Lau and Ong (2007) investigate the weakness of credit risk models in industrial firms and observe that the effect of counterparty risk can bring a company down. The most common exposure for industrial firms is lending items to customer. Additionally, firms can borrow from suppliers to pay after receiving payment from customers. Customers view the relationship as vital because they can get items and pay later. If the customer defaults, the firm may be unable to pay suppliers. This is especially the case if the default is on a large scale and the firm did not have enough capital to cover for it. This is an example of a scenario that credit risk models fail to anticipate and forestall.

Credit risk models assume that financial institutions operate as independent units. In reality, banks and financial institutions operate in tandem with each other. The institutions have an intricate relationship and the downfall of one institution may have ripple effects on other financial institutions. Credit risk models assume a linear relationship between a lender and borrower. In essence, such relationships are usually complex. It is a common practice for institutions to borrow from each other. Assuming a microfinance institution borrows money from bank to lend to its customers, a default by customers can be felt by the bank. Credit risk models fail to capture these relationships. This undermines their usefulness in a world where technology makes relationship between financial institutions more complex.

The financial world is becoming more intricate and complex especially because of technology. It is now possible for banks and other financial institutions to network and work across boundaries. Credit risk models also need to evolve in order to remain relevant in a dynamic world. Like observed in the discussion above, credit risk models have some strengths that financial institutions can employ to minimize exposure to credit risk. It is also important to appreciate the weakness so that financial organizations can exercise more monitoring and supervision to reduce credit risk exposure. It is however incumbent that specialists in the field of finance and technology continue to work towards a model that takes into account the realities of an evolving world.


Chan-Lau, J., & Ong, L. (2007) Credit Risk Transfer Market: Are They Slicing the Risks or Dicing with Danger?The journal of fixed income, 1(2),1-4.

Cunat, V. (2007). Trade credit: Suppliers as debt collectors and insurance providers, Review of Financial Studies 20(1), 491–527.

Esposito, F. (2011). Credit Risk Tools: An Overview. Journal of Advanced Studies in Finance, 2(1), 3-4.

Jorion, P., & Zhang, G. (2007). Good and bad credit contagion: Evidence from credit default swaps. Journal of Financial Economics, 1(84), 1-6.

Thompson, J. (2010). Counterparty Risk In Financial Contracts: Should The Insured Worry About The Insurer? Quarterly Journal Of Economics, 1(2),1-7.

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