DEVELOPING A COPULA-BASED DYNAMIC CONDITIONAL CORRELATION MODEL TO STIMULATE FINANCIAL RISKS AND ASSETS DEPENDENCIES.
Abstract
This study develops a Copula-Based Dynamic Conditional Correlation (DCC) model to simulate financial risks and analyze dependencies among equities, bonds, and commodities from 2013 to 2023. By integrating Copula functions with DCC models, the framework captures time-varying correlations and tail dependencies, addressing limitations of traditional risk models.Results demonstrate superior performance in estimating Value-at-Risk (VaR) and Conditional Value-at Risk (CVaR), particularly during the 2020 COVID-19 market crash, with fewer backtesting violations compared to multivariate GARCH and static correlation models. The Student’s t-Copula excels in modeling symmetric tail dependencies. The model offers practical benefits for portfolio risk management, stress testing, and regulatory oversight, despite computational complexity.
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