Modeling inflation rates and exchange rates in Ghana: application of multivariate GARCH models

Nortey, Ezekiel N. N.; Ngoh, Delali D; Doku-Amponsah, Kwabena; Ofori‐Boateng, Kenneth

doi:10.1186/s40064-015-0837-6

Cited by 21 publications

(29 citation statements)

References 11 publications

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“…The most popular Multivariate GARCH models are the diagonal BEKK, the diagonal VECH, CCC and DCC models. For a Multivariate time series Y y , y , y , … , y the Multivariate GARCH model is given by y = P / ε ( 1 ) where P denotes a k × k positive definite matrix and of the conditional variance of C t , k represent the number of series and t = 1,2,…,n where n is the number of observations (Nortey et al, 2015). It is with the specification of conditional variance that the Multivariate GARCH model changes.…”

Section: Multivariate Garch Modelmentioning

confidence: 99%

The Application of the Multivariate GARCH Models on the BRICS Exchange Rates

Metsileng

Moroke

Tsoku

2020

AJIS

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The study investigated the BRICS exchange rate volatility using the Multivariate GARCH models. The study used the monthly time series data for the period January 2008 to January 2018. The BEKK-GARCH model revealed that all the variables were found to be statistically significant. The diagonal parameters estimates showed that only Russia and South Africa were statistically significant. This implied that the conditional variance of Russia and South Africa’s exchange rates are affected by their own past conditional volatility and other BRICS exchange rates past conditional volatility. The BEKK-GARCH model also revealed that there is a bidirectional volatility transmission between Russia and South Africa. The results from the DCC-GARCH model revealed that Brazil, China, Russia and South Africa had the highest volatility persistence and India has the least volatility persistence. All the BRICS exchange rates show that the fitted residuals are not normally distributed except for Russia. The recommendations for future studies were articulated.

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Section: Multivariate Garch Modelmentioning

confidence: 99%

The Application of the Multivariate GARCH Models on the BRICS Exchange Rates

Metsileng

Moroke

Tsoku

2020

AJIS

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“…A Structural Vector Error Correction Analysis was used in their investigations. Nortey et al (2015) investigated the volatility and conditional relationship among inflation rates, exchange rates and interest rates using multivariate GARCH DCC and BEKK models using Ghana data from January 1990 to December 2013. The study revealed that the cumulative depreciation of the cedi to the US dollar from 1990 to 2013 is 7,010.2% and the yearly weighted depreciation of the cedi to the US dollar for the period is 20.4%.…”

Section: Literature Reviewmentioning

confidence: 99%

The Dynamics of Inflation, Money Growth, Exchange Rates And Interest Rates in Ghana

Abasimi¹,

Salim²,

Vorlak³

2018

TR1001

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Does money supply really influence inflation in Ghana? This study investigates the dynamics of inflation, money growth, exchange rate and interest rates in Ghana from 1990-2017. The autoregressive distributed lag model (ARDL) and error correction model (ECM) were employed in the study because the studied variables were found to be integrated and co-integrated at different intensities. The results revealed that money supply has no impact on inflation in the short and long run in the study period. Exchange rate and nominal interest rate were however found to influence inflation rate significantly in both the short and long run and in the same direction. It is recommended that the Bank of Ghana should maintain a stable economic growth by establishing a rigorous pecuniary polices as well as derivatives conveyance for financial institutions in the country to act in complacence.

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“…The relative demand for currencies reflects the causal demand for goods and services denominated in that currency. Depreciation in the currency value causes imported commodities to become relatively expensive, worsening the economy, especially if the country depends heavily on imports (Nortey et al, 2015). International capital flows can also have a strong influence on the demand for various currencies (Ehlers & Takas, 2013).…”

Section: Introductionmentioning

confidence: 99%

Co-integration analysis with structural breaks: South Africa’s gold mining index and USD/ZAR exchange rate

Chifurira

Chinhamu²,

Dubihlela

2016

Banks and Bank Systems

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This paper examines the presence of cointegration between South African gold mining index and USD/ZAR exchange rate. The results show that gold index and USD/ZAR exchange rate series are both I(1) and are cointegrated. The Granger causality test shows a two-way directional causality between gold index and USD/ZAR exchange rate for the period 9 June 2005-9 June 2015. By accounting for possible structural breaks, the Zivot-Andrews unit root test suggests two different breaking points in the data. By using the breaking dates to divide the dataset into 3 sub-periods, the results show that gold index and USD/ZAR exchange rate series are not cointegrated. The Granger causality test shows no causality between the two variables. This finding suggests that gold mining index does not play a key role in explaining the trends in the exchange rate and likewise exchange rate does not affect gold mining index. Keywords: USD/ZAR exchange rate, gold mining index, unit root tests, breaking points, cointegration. JEL Classification: F3, F4, F63, O47

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Modeling inflation rates and exchange rates in Ghana: application of multivariate GARCH models

Cited by 21 publications

References 11 publications

The Application of the Multivariate GARCH Models on the BRICS Exchange Rates

The Application of the Multivariate GARCH Models on the BRICS Exchange Rates

The Dynamics of Inflation, Money Growth, Exchange Rates And Interest Rates in Ghana

Co-integration analysis with structural breaks: South Africa’s gold mining index and USD/ZAR exchange rate

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