Modeling variations in the cedi/dollar exchange rate in Ghana: an autoregressive conditional heteroscedastic (ARCH) models

Abstract

This research article aimed at modeling the variations in the dollar/cedi exchange rate. It examines the applicability of a range of ARCH/GARCH specifications for modeling volatility of the series. The variants considered include the ARMA, GARCH, IGARCH, EGARCH and M-GARCH specifications. The results show that the series was non stationary which resulted from the presence of a unit root in it. The ARMA (1, 1) was found to be the most suitable model for the conditional mean. From the Box–Ljung test statistics x-squared of 1476.338 with p value 0.00217 for squared returns and 16.918 with 0.0153 p values for squared residuals, the null hypothesis of no ARCH effect was rejected at 5% significance level indicating the presence of an ARCH effect in the series. ARMA (1, 1) + GARCH (1, 1) which has all parameters significant was found to be the most suitable model for the conditional mean with conditional variance, thus showing adequacy in describing the conditional mean with variance of the return series at 5% significant level. A 24 months forecast for the mean actual exchange rates and mean returns from January, 2013 to December, 2014 made also showed that the fitted model is appropriate for the data and a depreciating trend of the cedi against the dollar for forecasted period respectively.

Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi

“…The results show that the exchange rate between the Ghanaian cedi and the USD can be predicted with a modest accuracy rate. Hence, like other previous studies [5] and [10,28,31,53], the outcome of this study also affirms the association between exchange rate variations and fundamental macroeconomic factors.…”

“…Therefore, there is a need fo tive models that can track the current dynamics of the ER an particularly in periods where the ER frequently fluctuates Thus, the results obtained (Figures 5-7) show that the exchange rate between the Ghanaian Cedis and GBP, USD, and EUR are not equally affected by the same macroeconomic variables. Additionally, the inflation rate affects the Ghanaian Cedis' exchange rate to EUR, GBP, and USD (Figures 5 and 6); however, not as deep as reported in [28,53]. On the other hand, the study outcome affirms Adusei and Gyapong's [5] claims that the monetary survey has a positive relationship with the Ghanaian cedi USD exchange rate.…”

Prevalence of hemorrhagic fever with renal syndrome in Yiyuan County, China, 2005–2014

“…Time-series analysis of incidence of various infections is extremely useful in developing hypotheses to explain and anticipate the dynamics of the observed phenomena and subsequently in the establishment of a quality control system and reallocation of resources [ 26 ]. There are a number of methods applied for time series analysis including ARIMA model [ 8 – 13 , 16 ], maximum entropy method (MEM) spectral analysis [ 27 ] and the autoregressive conditional heteroscedastic (ARCH) model [ 28 ]. The ARIMA model has its advantages in time-series analyses.…”

Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

“…In essence, they form a systematic framework for volatility modeling. Theoretically, they can be employed very successfully to measure the development of and predict the price of exchange rates that meet the above-stated assumptions, as evidenced by Petrica and Stancu (2017), Quaicoe et al (2015), You and Liu (2020), and Smallwood (2019). However, neural networks are a very suitable alternative that produce very interesting results, and of which the potential has not yet been fully exploited.…”