copulas-dependence-modeling

Model cross-asset dependence by calibrating Gaussian, t, or Archimedean copulas to your data. This captures non-linear dependencies and extreme joint movements that linear correlation misses, enabling accurate multivariate tail-dependence estimation for portfolio risk aggregation.

You need copula-based scenario generation when assessing portfolio risk under market stress. It creates dependence-preserving stress paths for risk aggregation that validate against extreme joint moves, going beyond what standard linear models can capture.

Fit candidate copula families by applying rank-consistent preprocessing to your data, then compare likelihood and tail metrics. This calibration validates the chosen copula family for production dependence-risk controls and multivariate risk aggregation.

Yes, rank-consistent preprocessing is required before fitting Gaussian, t, or Archimedean copulas. This preparatory step ensures accurate calibration and validates dependence-preserving scenarios against extreme joint moves for stress-path generation.

Linear correlation fails because it cannot model non-linear dependencies or tail dependence. Copulas solve this limitation by quantifying extreme joint movements, enabling realistic stress-path generation and accurate portfolio risk aggregation under market stress.