ARCH and GARCH models have become essential tools in time series analysis, particularly in financial
applications. These models are especially useful for analyzing and forecasting volatility, making them
valuable for studying exchange rate fluctuations and financial market risks. This study applies the
ARCH(1)-GARCH(1,1) model to examine the volatility of the Uzbekistani som (UZS) exchange rate, aiming
to identify patterns of volatility clustering and assess the impact of external economic shocks. Using a
dataset consisting of 351 monthly observations from January 1996 to March 2025, this research
evaluates the predictive performance of ARCH/GARCH models compared to traditional time-series
approaches. The findings indicate that exchange rate volatility in Uzbekistan exhibits significant
persistence, suggesting that past fluctuations strongly influence future uncertainty. The ARCH(1)GARCH(1,1) model effectively captures both short-term volatility shocks and long-term persistence,
making it a suitable choice for modeling exchange rate dynamics. Furthermore, incorporating
macroeconomic variables such as inflation, interest rates, and foreign direct investment (FDI) enhances
the model’s explanatory power. The results of this study have important implications for monetary
policy, risk management, and financial market stability. By accurately modeling exchange rate volatility,
policymakers and investors can make informed decisions to mitigate risks associated with currency
fluctuations. This research contributes to the growing body of literature on financial econometrics and
provides a comprehensive case study on Uzbekistan’s exchange rate dynamics using the ARCH(1)GARCH(1,1) model.
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