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February 11, 2026Kardan Journal of Economics and Manangement Sciences0 citationsOpen Access

Modelling and Forecasting of Exchange Rate Dynamics: Empirical Evidence from AFN/USD

AAAjmal Arian

Key Points

  • The study aims to explore and predict the dynamics of the AFN/USD exchange rate in Afghanistan from 2003 to 2020.
  • Utilized univariate time series analysis to outline exchange rate patterns.
  • Employed ARCH and GARCH models for analyzing volatility.
  • Developed both in-sample and out-of-sample datasets for model testing.
  • Confirmed persistence in volatility and identified volatility clustering in the exchange rate.
  • Showed that the standard GARCH (1,1) model effectively captures volatility dynamics of AFN/USD returns.

Abstract

This empirical study explores the changing aspects of the exchange rate, AFN versus USD, in Afghanistan, over an extended period ranging from March 1, 2003, until December 31, 2020, starting with the beginning of demonetization up to the eve of a political change in Afghanistan. The study focuses on illuminating the characteristics pertinent to modelling and predicting the instability of the exchange rate. The current study used univariate time series approaches to outline the pattern of the exchange rate and the ARCH and GARCH models. Both in-sample and out-of-sample datasets were developed to test the fitness and ability of models to predict the data. The results confirm that, over the selected time period, the exchange rate exhibits persistence in volatility with a stylized fact, namely, volatility clustering. The results highlight that the standard GARCH (1,1) model is effective in capturing and predicting the volatility dynamics of AFN/USD exchange rate returns, exhibiting strong volatility clustering and persistence. The models employed in this paper are highly useful for people who are engaged in the foreign exchange market and for policymakers while formulating economic policies in Afghanistan.

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Cite This Study

Ajmal Arian (2025) studied this question.

synapsesocial.com/papers/698c1c65267fb587c655edcfhttps://doi.org/10.31841/kjems.2025.196
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