Description Usage Arguments Value Author(s) References Examples
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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