Iran's Exchange Market in Five Episodes: Bayesian Estimation of Systematic Risk with MCMC Method

This paper estimates systematic risk in Iran’s foreign exchange market using a stochastic volatility model, analyzing five distinct episodes shaped by varying economic and political conditions. By tracing the evolution of volatility dynamics across these episodes, we reveal critical shifts in market behavior under different risk regimes. Our results show that during low-risk episodes, volatility shocks exhibit high persistence, causing market disturbances to linger. In contrast, as systematic risk intensifies, volatility shocks dissipate more rapidly—yet this reduced persistence coincides with a marked rise in average volatility. We identify three particularly turbulent episodes in the past seven years, each characterized by exceptionally high levels of systematic risk. Strikingly, both the mean and variance of volatility increased during these high-risk periods, signaling not only heightened instability but also deeper Knightian uncertainty. These findings carry significant policy implications: when direct reduction of volatility proves challenging, policymakers should prioritize reducing the volatility of volatility to mitigate uncertainty and stabilize expectations. Notably, our analysis indicates that a 1% reduction in volatility corresponds to a 1.7% decline in the variance of daily exchange rate returns, underscoring the leverage policymakers have over market uncertainty.