Quantifying Counterparty Credit Exposure by Edu Pristine

Author of lecture Quantifying Counterparty Credit Exposure

Edu Pristine

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... However uncertainty regarding the market variables will complicate the exposure measurement process which will increase as the time to maturity of contract increases?...

... Method I MtM + Add On factor. Current Exposure is approximated as maturity of transactions. Usually neglects aspects like netting, collateralization while estimating exposure. ...

...Method II Semi Analytical Methods. More sophisticated than add-on approach, but still needs approximations. Usually involves identifying the risk factors driving the exposures, finding ... risk factors like mean reversion, heteroscedasticity difficult to incorporate. Path dependency of exposure not captured since exposure calculations ...

... processing, Extraction of Statistics. Factor Choice – Determining the risk factors like FX rates, spot rates, implied volatility, interest rates that influence the credit exposure which can be simulated. Scenario Generation – Risk factor scenarios are ...

... exposure values for multiple trades with different counterparties. These exposures are then netted based on the netting conditions and net exposure for each counterparty is computed. Where: E j,k = exposure of the netting set at time j in scenario k, with m trades Thus Vi,j,k ...

... caution. Risk of missing jumps in exposure caused by these aspects is called "roll- off risk". This risk can be controlled using non time homogenous time grids. However this will cause the PFE to significantly change due to exposure jumps becoming engulfed ...

... and Bond PFE – PFE approximately equal to the notional value 7 ...

... in increasing exposure profiles...

... Profiles Cross Currency Swap ...

... on the kind of option. ...

... Derivative PFE Maximum exposure for the CDS occurs at a credit event ...

... Years Equal Unequal ...

... for Interest Rate Swapation and Forward Swap ...

... Brownian motion is used Where St = value of equity, t = drift, E = volatility and dWt = standard Brownian variable Returns are assumed to be normally distributed. Drift may be sum of the risk ...

... Model ensures that the FX rates are positive. Drift is based on historical data or forward rates whereas volatility is either market implied or historical. Alternatively a mean reverting model can be used to keep FX rates changes...

... Where is the default intensity and the mean reversion parameters? Jump process is incorporated using jdN where j is the size of the jump. ...

... with mean reversion, which is an extension of the Vasicek model. where t is a time dependent mean reversion parameter whereas parameters ...

... (EPE) Where E j,k = netted exposure for time j and simulation k & E' j,k is the new exposure when trade m+1 has been added. Summation term in bracket is the MtM values of the netting set at any correlations ...

... Both the EPE's with and without netting are simulated at different time points to first compute EE at different time points and thus calculate weighted average EE i.e. EPE. Total expected exposure for a netting set will be a linear combination of marginal expected ...