- Risks Inherent in Trading Activities
- Basic Measures of Market Risk
- Market Risk Limits
- Credit Risk and Counterparty Credit Risk
- Credit Risk Measurement and Management in Trading
- Determination of Presettlement Risk in Different Instruments
- Measuring Potential Future Exposure
- Market Liquidity Risk of Trading Activities
- Unbundling and Dynamically Hedging Risks
- Concentrated Positions and Market Risk
- Market Liquidity Risk Limits
Measuring Potential Future Exposure
Potential future exposure is an estimate of the risk that subsequent changes in market prices could increase credit exposure. In measuring potential exposure, institutions attempt to determine how much a contract can move in to the money for the institution and out of the money for the counterparty over time. Such is the importance of the interrelationships between the market-risk and credit-risk exposures involved in banks’ derivative activities that risk managers should be alert to situations in which banks may need to enhance their current computations of potential future exposures and loan equivalents used to measure and monitor their derivative counterparty credit exposure.
Estimating potential exposure is subjective, and different institutions approach its measurement in multiple ways. One method uses factors like percentages of the notional value of the contract, similar to the ‘‘add-on’’ factors used in bank risk-based capital. It is important that these factors help provide adequate estimates of potential exposure. The differences in the add-ons used for different instruments should reflect differences in the volatility of the underlying instruments. It should also reflect in the tenor (or maturity) across instruments, and should be adjusted periodically to reflect changes in market conditions and the passage of time.
Another method to estimate potential exposure, while being more complicated of derivatives is to statistically estimate the maximum probable value that the derivative contract might reach over a specified time horizon. This sometimes may be the life of the contract. This is done by estimating the highest value the contract will achieve within some confidence interval (for example, 95, 97.5, or 99 percent confidence) based on the estimated distribution of the contract’s possible values at each point in time over the time horizon, given historical changes in underlying risk factors. The specified percentile or confidence level of the distribution represents the maximum expected value of the contract at each point over the time horizon.
The time horizon used to calculate potential future exposure can vary depending on the bank’s risk tolerance, collateral protection, and ability to terminate its credit exposure. Some institutions may use a time horizon equal to the life of the respective instrument. Such a time horizon is appropriate for unsecured positions, for collateralized exposures and the use of lifetime. Worst-case estimates of potential future exposure may be ineffective in measuring the true nature of counterparty risk exposure—especially given the increasing volatility and complexity of financial markets and derivatives instruments. The life-of-contract potential future exposure may help measure and provide an objective and conservative long-term exposure estimate, but they bear little relationship to the actual credit exposures banks typically incur in the case of collateralized relationships. In such cases, a bank’s actual credit exposure is the potential future exposure from the time counterparty fails to meet a collateral call until the time the bank liquidates its collateral. This is usually much shorter than the contract’s life. There is a need therefore, for some institutions, to have more realistic measures of collateralized exposures in times of market stress. Shorter time horizons, over which measures to mitigate losses need to be taken, must be factored in. They should also incorporate estimates of collateral recovery rates given the impact of potential market events on the liquidity of collateral values. Institutions with vigorous monitoring systems can employ additional credit-risk-measurement methodologies that will tend to generate more precise and often smaller reported exposure levels. Some institutions already calculate such measures by assessing the worst-case value of positions over a time horizon of one or two weeks—their estimate of a reasonable liquidation period in times of stress. Other institutions are moving to build the capability of estimating portfolio-based potential future exposures by any one of several different time horizons or buckets, owing to the liquidity and breadth of the underlying instrument or risk factor. Some institutions measure the “expected” exposure of a contract in addition to its maximum probable exposure. The expected exposure is the mean of all possible probability-weighted replacement costs estimated over the specified time horizon. This calculation may reflect a good estimate of the present value of the positive exposure that is likely to materialize.
Expected exposure can be an important measure for use in an institution’s internal pricing, limit-setting, and credit-reserving decisions. However, expected exposure is by definition lower than maximum probable exposure and may underestimate potential credit exposure. It is for this reason that expected exposure estimates are not frequently used as loan-equivalent amounts in assessing capital adequacy from either an internal or regulatory basis.
Statistically generated measures of future exposure use sophisticated risk-measurement models that, in turn, involve the use of important assumptions, parameters, and algorithms. Institutions using such techniques should ensure that appropriate controls are in place regarding the development, use, and periodic review of the models and their associated assumptions and parameters. The variables and models used for both replacement cost and potential exposure should be approved and tested by the credit-risk management function and should be subject to audit by independent third parties with adequate technical qualifications. The data-flow process should also be subject to audit to ensure data integrity. Equally important are the approval and testing of information systems that report positions. The functions responsible for managing credit risk should validate any modifications to models made to accommodate new products or variations on existing products.
Free Guides - Getting Started with R and Python
Enter your name and email address below and we will email you the guides for R programming and Python.