Optimizing foreign exchange reserves: Protection against external shocks in Ghana

buir.contributor.authorAlhassan, Mohammed Kamil
dc.citation.epage25en_US
dc.citation.spage1en_US
dc.citation.volumeNumber13en_US
dc.contributor.authorAbdul-Rahaman, Abdul-Rashid
dc.contributor.authorHongxing, Yao
dc.contributor.authorAlhassan Alolo Akeji, Abdul-Rasheed
dc.contributor.authorAyamba, Emmanuel Caesar
dc.contributor.authorBernard Pea-Assounga, Jean Baptiste
dc.contributor.authorAlhassan, Mohammed Kamil
dc.date.accessioned2023-03-02T11:00:11Z
dc.date.available2023-03-02T11:00:11Z
dc.date.issued2022-11-02
dc.departmentDepartment of Mathematicsen_US
dc.description.abstractUsing Least Square Residual Minimization techniques, this paper develops an optimal reserve model, known as the OPREM model, which is essential in optimizing the costs of reserve holding. The paper also sets-out to test and compare the relative predictions of economic trends of the OPREM model as well as the predictions of alternative models in literature. Establishing the predictive accuracy of economic trends of these models are crucial for the gradual and cost-effective accumulation of reserves. The research concludes that, the decision to optimize the cost of reserves under a stable currency environment is reliant on the gold impact factor and not on inflation or interest rates. We also found on further analysis of the OPREM that the OPREM model is better positioned to eliminate the procyclicality and perverse rush in reserve build-ups experienced in developing and emerging countries by effectively setting the reserve stock against economic trends. The research fixes the optimal reserves around a benchmark of 0.7–1.2 of previous year's optimal value. However, in the absence of past optimal values, a benchmark between 2 and 6 times of average inflows for short-term analysis or analysis with small data observations. However, for long-term analysis or analysis with large data frequency (i.e., exceeding 13 data observations), the reserve stock should be fixed on a benchmark of 2–9 times of the average inflows. Copyright © 2022 Abdul-Rahaman, Hongxing, Alhassan Alolo Akeji, Ayamba, Bernard Pea-Assounga and Alhassan.en_US
dc.identifier.doi10.3389/fpsyg.2022.994043en_US
dc.identifier.issn16641078
dc.identifier.urihttp://hdl.handle.net/11693/112016
dc.language.isoEnglishen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.isversionofhttps://dx.doi.org/10.3389/fpsyg.2022.994043en_US
dc.source.titleFrontiers in Psychologyen_US
dc.subjectBank Of Ghanaen_US
dc.subjectCentral Bankingen_US
dc.subjectForeign Exchange Reservesen_US
dc.subjectLeast Squared Residualsen_US
dc.subjectMonetary Policyen_US
dc.subjectOptimalityen_US
dc.titleOptimizing foreign exchange reserves: Protection against external shocks in Ghanaen_US
dc.typeArticleen_US
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