Abstract
AbstractIn three different ways of lead–lag causal relationship, covariance/correlation and coherence, we apply the wavelets analysis via the Continuous Morlet Wavelet Transform to delineate the significant frequency–time domain lead–lag relationships for the West African Monetary Zone member countries for real US dollar exchange rates and their absolute log returns from January 2001 to April 2015. The results indicate that lead–lag associations at different periodicities vary across the countries. No one country comes off as leading conveniently for both real and absolute returns of the exchange rates. Our results corroborate other evidences of non-convergence of exchange rates in the monetary zone, which hinders the eventual implementation of the single currency in the ECOWAS region.
Highlights
Given the high dependence of the African continent on foreign exchange, a full-blown monetary integration is imperative but has been dragged in the glare of exchange rate risk
This study is in line with the broad studies on the convergence on exchange rates in the West African Monetary Zone (WAMZ)
Univariate analysis of real exchange rates The wavelet power is relative to unit-variance white noise and directly comparable to results of other time series (Roesch & Schmidbauer, 2014)
Summary
Given the high dependence of the African continent on foreign exchange, a full-blown monetary integration is imperative but has been dragged in the glare of exchange rate risk. Using monthly exchange rate data for four WAMZ (The Gambia, Ghana, Liberia and Nigeria) member countries from January 2001 to April 2015 This will help policy-makers make informed decisions in working towards the single currency. The analysis is based on monthly data of real exchange rates for four (The Gambia, Ghana, Liberia and Nigeria) member countries out of six (Guinea and Sierra Leone excluded) of the WAMZ from January 2001 to April 2015. We use Wavelets analysis of the exchange rate time series to better extract important features of co-movement than, for instance, correlation analysis. The wavelet-based methodology allows us to assess simultaneously the co-movement at the frequency level and over time in the exchange rate dynamics of the WAMZ; analysing transitory dynamic associations between two series. Unlike wavelets, the correlation analysis is unable to provide information about when correlations occur and lead–lag relationships— having different data series showing similar periodicities do not necessarily connote lead–lag relationship (Pinho & Madaleno, 2011)
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