Application of VaR to Non-Market Areas

Financial Institutions are often faced with risks other than normal market risk. That is especially true with derivative transactions. In derivative transactions, institutions need to increase their estimate of allowable loss amount by incorporating the following risks:

  • Pre-settlement risk
  • Settlement risk

Pre-settlement risk is the risk that a counterparty will default on a derivative transaction prior to the contract’s settlement at expiration (payment risk).

Settlement risk is a risk that a counterparty will default on a derivative transaction on the counterparty’s settlement (closing risk).

Attempts to mitigate these risks are known as bilateral payment netting and bilateral closing netting. By recognizing these risks institutions are attempting to estimate a value for this risk component and incorporate it into their calculations.

Aside from the trading business of financial institutions, recent attempts have been made to apply VaR concepts to the loan side.

A loan made to counterparty exposes a financial institution to credit risk. Credit risk is defined as the risk resulting from uncertainty in a counterparty’s ability or willingness to meet its contractual obligation.

In assessing credit risk from a counterparty, an institution must consider two issues:

  • Credit Quality: This encompasses both the likelihood of the counterparty defaulting as well as possible recovery rates in the event of a default.
  • Credit Exposure: In the event of a default, what is the replacement cost of the counterparty’s outstanding obligations likely to be?

Credit risk is typically reduced by collateralization. Under a collateralization arrangement, a party who owes an obligation to another party posts collateral. This collateral typically consists of cash versus securities to secure their obligation. One possible method for determining the VaR of a loan portfolio would be the Monte Carlo technique. This would generate random occurrences for various levels of default consequences that could be used to construct a probability distribution with its own mean and standard deviation. By attempting to evaluate credit risk, a VaR calculation can be performed on all assets and liabilities of a financial institution.

Apart from, this financial institutions are also using VaR to measure operational risk.