A System Dynamics Model Of Exchange Rate Determination And Forecasting

https://doi.org/10.33215/sjom.v3i4.367

Authors

Keywords:

Exchange Rates, system dynamics, fundamental factors, feedback relationships

Abstract

Objective: The objective of this paper is to develop a model of exchange rate determination and forecasting to provide reasonable forecasts for the exchange rate to facilitate long-term investments.

Design: The study develops the model using the system dynamics method. Grounded on the fundamental theories, the model incorporates nonlinear feedback relationships of interest rate, inflation, per capita income, terms of trade, and oil prices with the exchange rate.

Findings: The simulation results indicate the robustness of the model to mimic not only the long term past behavior of the exchange rate but also its ability to provide a reliable long-term forecast for the exchange rate. The model is portable and applies to any oil-exporting country after calibration.

Policy Implications: The study has practical implications for individuals, businesses, and the Government because they are all influenced by the exchange rate movements. Specifically, this model provides a useful tool for long term strategic financial planning of oil firms.

Originality: The study develops a model for exchange rate accounting for nonlinear feedback relationships among the variables.

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Author Biography

Aima Khan, University of Bergen, Norway

Aima Khan is a Ph.D. scholar at the system dynamics group, “University of Bergen”, Norway. She is a lecturer at “The women university”, Multan, Pakistan. She holds an MBA degree and a Master of Science (MS) with a specialization in finance. Her Ph.D. project focuses on firm valuation and policy analysis
using system dynamics. Her major area of interest for research involves finance using simulation as well as econometrics. She has publications in scientific journals.

Dimensions

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Published

2020-07-17

How to Cite

Khan, A. (2020). A System Dynamics Model Of Exchange Rate Determination And Forecasting. SEISENSE Journal of Management, 3(4), 44-55. https://doi.org/10.33215/sjom.v3i4.367