Bivariate Gaussian Copule VaR

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Description

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

Usage

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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

Value

Copula based VaR

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.

Examples

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# 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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