Credit Based Strategies In Pricing Contracts And Assets Admission Essay Help

Financial institutions have traditionally evaluated the creditworthiness of borrowers and counterparties using credit-based methodologies. The tactics pose various hazards to financial institutions notwithstanding their popularity. According to Jorion and Zhang (2007), banks and financial institutions fail due to inadequate credit evaluation rules. Regarding the pricing of assets and contracts, many lenders have been ignorant to the shifting economic climate. This has caused substantial financial losses. Credit risk strategies aim to optimize the "risk-adjusted rate of return" of financial institutions by keeping credit risk exposure within acceptable bounds (Thompson, 2010, p.4). This article will explore the advantages and disadvantages of credit-based pricing strategies for contracts and assets.

Credit risk, as defined by Jorion and Zhang (2007), is "the possibility that a borrower or counterparty will fail to meet their obligations in accordance with agreed terms" (4). A financial institution or other lender should strive to maintain risk exposure within acceptable and reasonable bounds. There are numerous credit risk sources for banks and other lending institutions. Most loans are issued by banks. However, all activities within a bank involve substantial credit risk. Esposito (2011) cites emerging sources of credit risk, including "financial futures, swaps, bonds, equities, options, and in the extension of commitments and guarantees, and in the settlement of transactions" (5). Given the diversity of credit risks, it is crucial for stakeholders to foster an environment that facilitates credit risk minimization. Despite the apparent dangers involved, credit-based pricing systems have a number of advantages when it comes to pricing contracts and assets.

If properly administered, credit-based solutions enable lenders to make prudent lending decisions and maintain security. Credit risk systems have evolved throughout time to produce sophisticated rating systems that accurately assess risk. Lenders are now able to assess risk in a manner that generates optimal returns. In various respects, credit risk ratings have changed credit risk management. The first involves credit approval and underwriting. Before a lender enters into a contract with a borrower, there are systems in place to determine with a high degree of precision whether the borrower's assets can cover all loan costs in the event of nonpayment.

The second strength is the pricing of assets. Credit-based solutions permit the lender to accurately price a loan in accordance with the borrower's assets. The solutions permit the lender to create a balance between assets and loans without negatively impacting the financial institution. Thus, it has become simpler to establish the proper relationship between credit management and administration. Credit ratings enable financial organizations to analyze a client's creditworthiness with greater precision. Credit specialists are better equipped to oversee the administration of loan facilities in a manner that maximizes the lender's profits. In recent years, professionals have been able to automate credit facility appropriateness evaluations. Due to the synchronization of systems at numerous financial institutions, interbank borrowings have become notably simpler.

Credit risk models are another credit-based technique for calculating asset and contract pricing. The primary advantage of these models is that they allow financial organizations to "measure the distributions of their potential credit losses at the institution's highest level" (Esposito, 2011, p.4). Therefore, institutions are able to determine how much capital to store as a buffer against collapse in the event of a large loss. Particularly among banks and Collateralized Debt Obligations, the models have achieved significant adoption. In spite of safeguards against defaulting, the models need calibration. This allows for clustering by default. Jorion and Zhang (2007) note that the second generation of credit risk models has mitigated the issue of clustering, albeit not fully.

Appropriate staffing can strengthen credit risk models and lower risk exposure (Chan-Lau and Ong, 2007). The management of financial institutions must devise effective strategies to minimize credit risk exposure. This is possible through the development of credit evaluation and monitoring procedures. These procedures will develop a credit culture that values accuracy in credit evaluation, decreases credit rating errors, and assigns help to relevant departments in order to reduce credit risk exposure. According to Esposito (2011), the strongest protection against credit risk exposure is a highly qualified and motivated workforce. Credit rating abilities should be an inherent part of performance evaluation so that employees treat it with the respect it merits. A staff that is accurate and timely protects financial institutions from losses caused by inaccurate credit risk assessment. However, a competent worker utilizing obsolete technology and data will still pose an exposure risk. Therefore, it is essential for financial institutions to embrace dynamism and integrated credit risk ratings in order to support the efforts of the employees and provide the synergy necessary to mitigate credit risk exposure.

Despite the aforementioned benefits, credit-based solutions have several shortcomings that enhance credit risk exposure. Chan-Lau and Ong (2007) suggest that, despite the fact that credit risk models have become increasingly complicated over time, it is incredibly difficult to calibrate them. This is due to the fact that "correlations cannot be measured directly for specific obligors" (4). Traditional credit models result in more losses, while modern technical methods cluster defaults. Clustering results in losses regardless of whether a financial institution is a direct lender. As noted previously, financial institutions hold some capital as a hedge against credit risk exposure. With clustering, however, the capital savings may be depleted if a financial institution experiences the ripple effect of lending money to a failed bank.

Counterparty risk is a further difficulty for credit risk model solutions. According to Cunat (2007), this type of risk happens "when the default of a company causes financial distress for its creditors" (4). A single default can precipitate a cascade of others, crippling the operations of a financial institution. The interconnected nature of financial institutions makes it easier for defaults to cascade and spread from one institution to another. If counterparty risk cannot be eliminated, the challenge for the next generation of credit risk models is to ensure that exposure to it remains relatively modest.

Chan-Lau and Ong (2007) examine the deficiency of credit risk models in industrial enterprises and note that counterparty risk can bring a business to its knees. Lending items to customers is the most prevalent risk for industrial enterprises. In addition, corporations might borrow from suppliers to pay after receiving customer payments. Customers value the relationship since they can obtain products and pay later. If the consumer fails to pay, the company may not be able to pay its suppliers. This is especially true if the default is widespread and the company lacks the resources to cover it. This is an example of a situation that credit risk algorithms cannot predict and prevent.

Models of credit risk imply that financial organizations act as autonomous units. In practice, banks and financial institutions collaborate. The institutions are interconnected, and the failure of one institution could have repercussions for other financial institutions. Models of credit risk presume that the relationship between a lender and borrower is linear. Such interactions are typically complex at their core. It is typical for institutions to borrow from one another. If a microfinance organization borrows money from a bank to lend to its consumers, the bank will feel the effects of a customer default. Credit risk models do not account for these linkages. This diminishes their utility in a world where technology has complicated the relationships between financial institutions.

Because of technology, the financial sector is growing more convoluted and complex. Banks and other financial institutions can now network and collaborate across borders. Models of credit risk must also adapt to be relevant in a dynamic world. As noted in the preceding section, credit risk models has strengths that financial institutions can utilize to reduce their exposure to credit risk. It is also essential to recognize the weakness so that financial institutions can increase their monitoring and oversight to decrease their exposure to credit risk. However, specialists in the fields of finance and technology must continue to strive towards a model that accounts for the reality of a world in constant change.

References

Chan-Lau, J., and Ong, L. (2007). Credit Risk Transfer Market: Are They Slicing or Dicing the Risks?

1(2),1-4 in The journal of fixed income.

Cunat, V. (2007). Trade credit: Suppliers as debt collectors and insurers, Review of Financial Studies, volume 20, number 1, pages 491–521

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

Jorion, P., & Zhang, G. (2007). Credit default swaps provide evidence of both good and poor credit contagion. 1(84), 1-6, in Journal of Financial Economics.

J. Thompson (2010). Should the Assured Be Afraid of the Insurer Regarding Counterparty Risk in Financial Contracts? 1(2),1-7, Quarterly Journal of Economics.

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