Challenges in Managing Credit Exposure

Credit exposure management is complex. There are several challenges and issues that surround exposure management. Some of them are discussed below:

  • Full exposure coverage and aggregation: Banks typically ignore minor exposures during credit aggregation. These ignored exposures may however at some point grow and exceed global counterparty limits. A small client may offer the shares of XYZ to the bank as collateral at the time of seeking a loan. XYZ independently may be a client of the bank with large borrowings. Only during a default event will the bank fully realize the extent of exposure to XYZ it has.
  • A uniform understanding of risk, its measurement and factors that affect it: A bank may have aggressively grown in size through a series of mergers and acquisitions. It is quite possible that uniform measures in assessing and understanding risk, setting limits is not followed. Sometimes traders may introduce new concepts like negative credit exposure. A loan that is received from another bank is considered as negative exposure as it can offset this negative exposure with another loan resulting in net zero credit exposure. Most banks may not offset this negative exposure with the original counterparty; instead it may bundle and pass on the negative exposure to other clients.
  • Capturing the credit exposure of credit derivatives: Exposure from credit derivatives are offsetting rather than additive. A firm that buys protection to lower credit exposure may find that the systems have added both exposures after computing the credit exposure of the credit derivative rather than offsetting it. This occurs as they cannot correctly reflect counterparty risk and issuer.
  • How to treat ‘wrong way exposure’: Wrong way exposure is considered exposure that goes against you when it hurts the most. This is caused by ignoring the positive correlation between default probability and credit exposures. If a long put option is put on counterparty A issued by A; if the share price drops drastically, the put option would gain in value and up the credit exposure. Would A be able to pay if the share price drops further? It is therefore important to understand the correlation between market risk and credit risk. Banks are now trying to do this using stress testing methods.
  • Netting Agreements
  • The mapping of complex exposures onto market risk, simulating
  • Difficulty in forecasting variables like prepayment of mortgages

All of these are challenges for assessing credit exposure.

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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.