Abstract
This study investigates the network-based interdependence among fifteen emerging market currencies using a quantile factor-augmented vector autoregression (QFVAR) model to capture the asymmetric nature of volatility spillovers across different market conditions (5th, 50th and 95th quantiles). By incorporating macroeconomic, financial and sentiment factors extracted through principal component analysis (PCA), the study examines how systemic risk and contagion evolve under varying levels of market stress. The results reveal that connectedness intensifies during extreme market conditions, with the Total Connectedness Index (TCI) increasing significantly during crisis periods, indicating stronger contagion and volatility transmission. Financial and sentiment factors show the strongest positive influence on connectedness, particularly in high-volatility regimes (τ = 0.95), while macroeconomic factors display stabilising effects around the median quantile. These findings suggest that systemic transmission channels in emerging markets are largely driven by financial and behavioural dynamics, especially during periods of heightened uncertainty. The study contributes to understanding how tail-dependent spillovers shape currency interconnectedness and provides policy implications for regulators and investors, emphasising the importance of monitoring cross-market linkages, strengthening macroprudential frameworks and developing early-warning systems to mitigate systemic vulnerabilities in emerging economies.