Dynamic Conditional Correlation with EGARCH Volatility for Conditional Value-at-Risk Portfolio Optimization: Evidence from IDX Blue-Chip Stocks - Jumadil Saputra; Moch Panji Agung Saputra (International Journal of Quantitative Research and Modeling, 2026)
This study proposes a novel portfolio optimization framework that integrates the Dynamic Conditional Correlation (DCC) model with Exponential GARCH (EGARCH) volatility estimation to compute time-varying Conditional Value-at-Risk (CVaR) as a downside risk measure. Unlike classical Mean-Variance (MV) optimization which assumes constant correlations and normally distributed returns, the proposed DCC-EGARCH-CVaR model captures asymmetric volatility responses to market shocks and dynamic cross-asset co-movements, yielding a more realistic representation of financial risk in emerging markets. The study employs daily closing price data from five blue-chip stocks listed on the Indonesia Stock Exchange (IDX) — BBCA, BBRI, TLKM, ASII, and UNVR spanning January 2020 to December 2024. Results demonstrate that the EGARCH(1,1) model with Student-t innovations outperforms GARCH(1,1) and GJR-GARCH based on AIC/BIC criteria, confirming the presence of leverage effects in all return series. The DCC-EGARCH-CVaR optimized portfolio achieves a 9.0% higher Sharpe ratio compared to the classical minimum-variance portfolio, while simultaneously reducing the 95% CVaR by 6.6%. Portfolio weights derived from the DCC-EGARCH-CVaR framework are more diversified and responsive to regime shifts in market conditions compared to static MV optimization. These findings provide practical implications for risk-aware asset allocation in Indonesian capital markets.