GaussianCopulaVaR function

Bivariate Gaussian Copule VaR

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

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

Arguments

• 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

Returns

Copula based VaR

Examples

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

Author(s)

Dinesh Acharya

References

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

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