Credit Limits and Provisions

There are several factors that determine how credit limits and provisions are set. The limit could be a notional amount against which an individual counterparty’s exposure is restricted within. Therefore, there will be a total exposure that should not exceed that notional amount.

Expected loss is another variable that can be used to set limits. If the party experiences a loss then the credit line is withdrawn.

The terms of credit, the structure of borrowing needs to be taken into account while setting limits. Certain experts believe for instance that long term credit is far riskier than short term, since the probability of default increases over time.

In the instance of a currency forward that allows a counterparty to exchange one currency for another at a predetermined rate has a potential credit exposure profile that increases with time. The potential credit exposure will be longer, the greater the time to maturity. Therefore a limit that reduces with decreasing time to maturity provides no incentive to do long term currency forwards.

One of the key responsibilities of a credit officer is to review loss provisions along with a monitoring of daily positions. A key strategic position may quickly turn into a legacy position. This can lead to substantial write-offs. The credit officer therefore has to look at previously earned spreads and see if provisions have to be revised for expected losses.

Sometimes provisions do not track inherent credit risks. If a bank invests in a hedge fund, they need to either sell or buy further in the bed to dynamically delta hedge the short put option. Sometimes the value of the fund may go down even as the bank buys more shares in the fund. The credit risk component might not be fully recognized.

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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.