Description Usage Arguments Value Author(s) References Examples

View source: R/GaussianCopulaVaR.R

Derives VaR using bivariate Gaussian copula with specified inputs for normal marginals.

1 | ```
GaussianCopulaVaR(mu1, mu2, sigma1, sigma2, rho, number.steps.in.copula, cl)
``` |

`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 Profit/Loss on two positions |

`number.steps.in.copula` |
Number of steps used in the copula approximation ( approximation being needed because Gaussian copula lacks a closed form solution) |

`cl` |
VaR confidece level |

Copula based VaR

Dinesh Acharya

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

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

1 2 | ```
# VaR using bivariate Gaussian for X and Y with given parameters:
GaussianCopulaVaR(2.3, 4.1, 1.2, 1.5, .6, 10, .95)
``` |

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