A copula that takes its dependence from a multivariate normal distribution. It is simple but gives little probability to joint extreme events.
Where you will meet it
Gaussian copula comes up in 15 learning outcome notes across the FRM syllabus.
A few of them:
Measuring Returns, Volatility, and Correlation FRM Part I · Quantitative analysis
“It underlies Gaussian copula models of default correlation.”
Measuring Credit Risk FRM Part I · Valuation and risk models
“The Gaussian copula maps each variable, percentile by percentile, onto a standard normal variable.”
Credit Risk FRM Part II · Credit risk measurement and management
“The Gaussian copula links individually estimated distributions of time to default into one joint distribution.”
Parametric Approaches (II): Extreme Value FRM Part II · Market risk measurement and management
“The Gaussian copula has no asymptotic tail dependence for any correlation below 1, so it understates the chance of joint crashes and overstates diversification.”
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