Calculating Portfolio Risk VaR Using Copula Functions

Calculating portfolio risk VaR using copula functions represents one of the current research hotspots in the financial field. This approach takes into account the correlations between multiple assets, enabling more accurate risk estimation for entire investment portfolios. In implementation, copula functions typically involve modeling joint distributions through dependency structures, where common choices include Gaussian, t-, or Clayton copulas. The computational workflow generally includes: first estimating marginal distributions for individual assets, then fitting copula parameters to capture dependencies, and finally simulating portfolio returns through Monte Carlo methods to derive VaR estimates. Consequently, this methodology holds significant application value in risk management and asset allocation domains. Furthermore, research in this area provides new theoretical support for institutions such as hedge funds and securities companies, helping to further enhance their risk management capabilities and investment returns through more sophisticated dependency modeling techniques.