Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive heteroscedasticity (GARCH) models on daily stock return emerging markets. A log-returns three leading indices such as KSE100, KSE30, KSE-ALL from Pakistan Stock Exchange SSE180, SSE50 SSE-Composite Shanghai during period 2009–2019 are used DCC-GARCH modeling. Joint DCC parametric results show that even highly volatile markets, bivariate model provides better performance than traditional VaR models. Thus, DCC-GRACH indicate effectiveness This is helpful to stockbrokers investors understand actual behavior stocks Subsequently, can also provide insights into forecasting while considering combined correlational effect all stocks.