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The Impact of a Multiple-Exchange-Rate Regime on the Transmission Mechanism of Macroeconomic Shocks: A Quantile Connectedness Approach to Iran (1986–2025; 1365–1403) | ||
| پژوهشنامه اقتصاد کلان Macroeconomics Research Letter | ||
| Article 1, Volume 21, Issue 50, June 2026 PDF (1.76 M) | ||
| Document Type: Scientific paper | ||
| DOI: 10.22080/mrl.2026.29681.2193 | ||
| Authors | ||
| ÙŽAhmad Mohammadi Pour1; Mohsen Mehrara* 1; ÙŽAli Tayebnia1; Mehdi Ghaemi Asl2 | ||
| 1University of Tehran | ||
| 2Kharazmi University | ||
| Receive Date: 14 July 2025, Revise Date: 07 September 2025, Accept Date: 04 October 2025 | ||
| Abstract | ||
| This study investigates how a multiple-exchange-rate regime—by creating a gap between the official and free-market rates—reshapes the transmission of macroeconomic shocks in Iran. Using a Quantile Vector Autoregression (QVAR) and quantile connectedness measures based on forecast-error variance decompositions, we analyze a monthly multivariate system for 1986–2025 (1365–1403 SH) including the exchange-rate gap, money supply (liquidity), global oil prices, the real interest rate, and consumer price inflation. With a 12-month forecast horizon and a 60-month rolling window (robustness at 48 and 72 months), we find that spillovers are markedly stronger in the distribution tails than around the median: The Total Connectedness Index rises from roughly 20% in normal conditions to above 70% in tail regimes. In crisis states (left tail), oil shocks and declines in the real interest rate are the primary sources of volatility, liquidity growth is the largest net receiver, and the exchange-rate gap often acts as a net sender, activating the currency-to-inflation transmission channel. In boom states (right tail), liquidity becomes the principal net sender and the real interest rate the main net receiver, while the exchange-rate gap plays a relatively limited/near-neutral role; at the median quantile, the gap remains the key net transmitter. Robustness checks across rolling windows and oil-focused specifications (external and oil-conditioned) confirm these regime-dependent patterns. Because our measures are variance-share based, they quantify the intensity and direction of spillovers rather than structural causal coefficients. Policywise, spillover strength and direction are state-dependent and time-varying; hence exchange-rate and monetary policy should be data-driven, adaptive to regime conditions, and coordinated with fiscal tools to contain tail-risk spillovers and systemic vulnerabilities. | ||
| Keywords | ||
| Exchange rate; Inflation; Multiple exchange rate regime; Exchange rate gap; Quantile Vector Autoregression | ||
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