Open Journal of Statistics

Volume 13, Issue 5 (October 2023)

ISSN Print: 2161-718X   ISSN Online: 2161-7198

Google-based Impact Factor: 0.53  Citations  

Trend Analysis of Exchange Rate of the Ghana Cedi against the US Dollar Using Time Series

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DOI: 10.4236/ojs.2023.135035    132 Downloads   2,028 Views  

ABSTRACT

Exchange rate volatility or its stability is a key determinant of the state of a country’s economy. The Ghana cedi’s performance against the US dollar in recent times has been the worse in the past decade. This has resulted in high inflation, high cost of living and high cost of production in Ghana. Despite the recent economic recovery growth, the cedi continues to strife in high rate of exchange against the dollar. This study examines and models a trend, and makes predictions of future rates of the cedis against the US dollar. Methodology: The study used a 13-year data of exchange rates of Ghana cedi and the US dollar spanning from 2010 to mid-2023 from the Bank of Ghana’s economic data on exchange rates, Ghana Stock Exchange and the World Bank. The ARIMA and SARIMA models were used to model the trends and for forecasting, taking into the consideration the asymmetric and seasonal effect of the data. Results: The outputs show that, the Ghana cedi will continue to rise but steadily against the US dollar for the remaining months of 2023 except in December, and continue to decline afterwards through into 2024. Conclusion: The cedi continues to weaken in value and the strength of its purchasing power. A weaker currency depicts a “junk” economy which affects its foreign investment. As the US dollar continues to rise, the Government and policy makers must implement effective policies to stabilize its rise against the cedi. Export of commodities must increase in addition to import restrictions to balance trade deficit and to strengthen the Ghana Cedi.

Share and Cite:

Ayiah-Mensah, F. , Arthur-Nunoo, P. , Acquah, J. and Addor, J. (2023) Trend Analysis of Exchange Rate of the Ghana Cedi against the US Dollar Using Time Series. Open Journal of Statistics, 13, 734-745. doi: 10.4236/ojs.2023.135035.

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