Counterparts Credit Risk Essay

1902 words - 8 pages

Counterparty credit risk in portfolio risk management
Prominent financial institution failures reminded market participants that over-the-counter derivatives bring counterparty credit risk. Even as these markets move towards settlement through clearing houses, significant volumes of existing and new transactions remain bilaterally settled, especially as non-standard derivatives may not qualify for central clearing. UBS Delta is providing tools for clients to measure counterparty exposure alongside other investment risk
Prudent risk management of credit portfolios includes measurement and limitation of exposure to individual issuers to manage concentration risk. Investment portfolios will ...view middle of the document...

Exposure modelling Here we use a simple example to explain exposure measures: The only trade with counterparty “MadeUpBankLtd” is a foreign exchange forward, buying EUR10m for USD, one-year forward at 1.3600. We allow (for educational purposes only) currency returns to take seven equally spaced values, with defined probabilities, as in figure 1. We calibrate the distribution ensuring that the mean price equals the forward and standard deviation of returns equals one-year volatility. For each rate we calculate the value of the trade (and thus the netting set) and from that the counterparty exposure: positive netting set value equals exposure; negative and the exposure is zero, as money is owed to the counterparty. (Note: In either case, we will lose the transaction at default. If we wish to keep the same position in EUR|USD, the trade will need replacing.) The average of these exposure numbers – expected exposure (EE) – is just over USD0.5m. EE is driven by EUR|USD and by the volatility of EUR|USD, due to asymmetry of the exposure profile. Potential future exposure (PFE), a measure of the extreme of the exposure, is just under USD3m, at the 97.5% confidence level. PFE is commonly used to limit exposure to individual counterparties. EE numbers have the advantage that, when being aggregated across netting sets (that cannot be netted together), the EEs are additive, unlike PFEs. EEs are therefore often used for portfolio level measures and limits. Sensitivities We can use this simple model to derive useful measures of sensitivity of exposure to market drivers and volatility changes. Shifting EUR|USD up by 0.01 pushes the EE up on this trade by ~USD32,000 and the exposure rises by ~USD49,000 per one point rise in volatility. Where netting sets have large numbers of market drivers, sensitivity measures are very useful, especially when volatile markets cause large exposure moves just as counterparty credit may need extra attention. Exposure profiles Performing the same exercise for different time horizons will give EE and PFE exposure profiles. Taking the second row of table A as an example, we can show the exposure profile (EE and PFE) across the maturity spectrum for this set of receive-fixed ITM swaps (see figure 2). Many risk limit frameworks use peak PFE/peak EE measures, as shown in table A.

1 An illustrative model – EE and PFE


Value of exposure

EE (expected exposure) – mean of the exposure numbers
20% 5% 20% 5%



PFE (potential future exposure) – maximum exposure at a certain confidence level

0.8000 0.9400 1.0800 1.2200 1.3600 1.5000 1.6400 1.7800 1.9200

Buy EUR10m for USD13.6m, one-year forward (only trade with c/p)
(in one year)

2.5% 0.9945 -4.2m 0 0

5% 1.1015 -2.8m 0 0

20% 1.2200 -1.4m 0 0

45% 1.3512 0 0 0

20% 1.4965 +1.365m +$1.37m $0.27m

5% 1.6574 +2.974m +$2.97m $0.15m

2.5% 1.8356 +4.756m +$4.6m $0.12m


Trade value

Source: UBS Delta

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