Volatility risk premium and market risk forecasting: Good vs. bad volatility in emerging and developed markets

Standard asset pricing models often fail to capture acute tail risks and asymmetric volatility in financial markets, particularly in weaker economies. This study investigates whether the volatility risk premium (VRP) can predict fat-tail risks and asymmetric tendencies in emerging and developed markets. Employing conditional value-at-risk (CoVaR), VaR-regression, Baba, Engle, Kraft, and Kroner generalized autoregressive conditional heteroskedasticity (BEKK-GARCH), and dynamic conditional correlation (DCC) frameworks, realized volatility was bifurcated into good (positive) and bad (negative) components. Findings reveal that bad volatility drives systemic and individual risks at nearly twice the rate of good volatility. Emerging markets exhibit persistent, integrated GARCH (IGARCH)-like volatility, whereas developed markets remain mean-reverting. Models incorporating VRP significantly outperform GARCH-type models in out-of-sample forecasts, showing a 25–30% predictive improvement for emerging markets versus 19–20% for developed markets. By identifying impending tail risks missed by historical data, the VRP and asymmetric volatility elements are essential for enhancing macro-stability policies and portfolio risk management in structurally precarious, highly sensitive markets.