Description Usage Arguments Value Author(s) References Examples

View source: R/CdfOfSumUsingGaussianCopula.R

If X and Y are position P/Ls, then the VaR is equal to minus quantile. In such cases, we insert the negative of the VaR as the quantile, and the function gives us the value of 1 minus VaR confidence level. In other words, if X and Y are position P/Ls, the quantile is the negative of the VaR, and the output is 1 minus the VaR confidence level.

1 2 | ```
CdfOfSumUsingGaussianCopula(quantile, mu1, mu2, sigma1, sigma2, rho,
number.steps.in.copula)
``` |

`quantile` |
Portfolio quantile (or negative of Var, if X, Y are position P/Ls) |

`mu1` |
Mean of Profit/Loss on first position |

`mu2` |
Mean of Profit/Loss on second position |

`sigma1` |
Standard Deviation of Profit/Loss on first position |

`sigma2` |
Standard Deviation of Profit/Loss on second position |

`rho` |
Correlation between P/Ls on two positions |

`number.steps.in.copula` |
The number of steps used in the copula approximation |

Probability of X + Y being less than quantile

Dinesh Acharya

Dowd, K. Measuring Market Risk, Wiley, 2007.

Dowd, K. and Fackler, P. Estimating VaR with copulas. Financial Engineering News, 2004.

1 2 3 | ```
# Prob ( X + Y < q ) using Gaussian Copula for X with mean 2.3 and std. .2
# and Y with mean 4.5 and std. 1.5 with beta 1.2 at 0.9 quantile
CdfOfSumUsingGaussianCopula(0.9, 2.3, 4.5, 1.2, 1.5, 0.6, 15)
``` |

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