Credit Risk Measurement and Management in Trading

BASEL II advises two methods of capital allocation for banks to use to measure credit risk and allocate capital to protect them against such credit risk. They are the standardized approach and the internal rating approach.

All financial institutions must based on their size arrive at methods to measure, monitor and protect themselves against credit risk. Smaller trading companies for instances will require simple checks and balances in place. Larger institutions with more complex loan instruments on the other hand will need to invest and maintain automated systems and policies and highly qualified staff that can use the same.

The importance of a sound credit risk management framework cannot be emphasized enough. It needs to be discussed and formalized at the highest level that is the board and implemented to the last credit officer. The formation of a credit risk policy, a credit risk committee, a credit-approval process, and credit risk management staff who measure and monitor credit exposures throughout the organization is vital.

Despite multiple organizational approaches to manage credit risk, the credit risk management of trading activities should be integrated into the overall credit-risk management of the institution to the best extent possible. Banking organizations usually have extensive written policies covering their assessment of counterparty creditworthiness for the initial due-diligence process (that is, before conducting business with a customer) and ongoing monitoring. The challenge is in how such policies are structured and implemented.

The credit risk management procedure has the following steps:

  1. Developing and approving credit-exposure measurement standards
  2. Setting counterparty credit limits
  3. Monitoring credit-limit usage and reviewing credits and concentrations of credit risk
  4. Implementing minimum documentation standards

The staff that approves exposures must be separate from the staff responsible monitoring risk limits and measuring exposures. Traders and marketers can assume risks that are within institutional credit-risk controls. The credit-risk-management function must be independent of the credit function in the trading area that have high expertise in trading-product credit analysis and meet the demand for rapid credit approval in a trading environment. This is so that they may carry out these responsibilities without compromising internal controls of these marketing and trading personnel who are directly involved in the execution of the transactions.  The credit staff in the trading area may possess great expertise in trading-product credit analysis, but the persons responsible for the institution’s global credit function should have a solid understanding of the measurement of credit-risk exposures in trading products and the techniques available to manage those exposures.

Credit measurement is an integral part of effective credit risk management in trading operations. For example for most cash instruments, presettlement credit exposure is measured as current carrying value. However, in the case of many derivative contracts, especially those traded in OTC markets, presettlement exposure is measured as the current value or replacement cost of the position, plus an estimate of the institution’s potential future exposure to changes in the replacement value of that position over the term of the contract.

The methods to measure counterparty credit risk need to be commensurate with the volume and level of complexity of the instruments involved. The most important thing with credit measurement is that it must assess and calibrate the risk and therefore limits as close to the true nature of the credit exposures involved. Over protecting against credit risk would mean losing out on some quality customers. The choice of technique to measure the credit risk must therefore be realistic. Unrealistic measures in the credit risk management process require a review of the credit risk measurement system and must be reevaluated as soon as possible.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.