Estimating the probability distribution of the exchange rate between Ghana Cedi and American dollar

Sarpong, Solomon

doi:10.1016/j.jksus.2018.04.023

Cited by 11 publications

(12 citation statements)

References 14 publications

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“…The currency rate follows the random walk process, meaning that the rate expectation of one future period ( ) is the same as the spot rate ( ): (Kuncoro, 2001) (Wang, 2015) (He, et.al, 2018) (Sarpong, 2019). = + + (1) Where: = Future spot (spot rate value in next period); = constant; = coefficient; = current spot rate (Cash); = error This equation states that when the market is efficient, the current spot rate will reflect all available information.…”

Section: Spot Ratesmentioning

confidence: 99%

“…This hypothesis teaches that future spot rate expectations are the weighted average of the current spot rate in the formula (1) or the spot market efficiency test as well as the forward rate of the formula (2) or the forward market efficiency test and Together the spot rate and the forward exchange rate are used to test the Future Spot on the formula (3) or the composite market efficiency test. Future spot based on composite market efficiency: (Kuncoro, 2001) (Wang, 2015) (He, et.al, 2018) (Sarpong, 2019) = + + +…”

Section: Future Spot Ratementioning

confidence: 99%

“…The exchange rate of a country's currency is one of the important indicators in an economy. The exchange rate also has broad implications, both in the domestic and international economic context, considering that almost all countries in the world do foreign exchange transactions, often referred to as foreign currencies (Fama, 1984) (Madura, 2011) (Sarno, et.al, 2012) (Ebiringa, et.al, 2014) (Muchlas, & Alamsyah, 2015) (Ramasamy & Abar, 2015) (Tafa, 2015) (Wang, 2015) (Adisetiawan & Surono, 2016) (Ma, et.al, 2018) (He, et.al, 2018) (Keivan & Safari, 2018) (Caporalea, et.al, 2018) (Sarpong, 2019) (Inouea & Rossib, 2019). A very important issue noted in foreign exchange matters is exchange rates.…”

Section: Introductionmentioning

confidence: 99%

“…The foreign exchange according to Kuncoro (2001), Sarno, et.al (2012), Ebiringa, et.al (2014), Jayanti, et.al (2014), Muchlas & Alamsyah (2015), Ramasamy & Abar (2015), Tafa (2015), Wang (2015), Caporalea, et.al (2018), He, et.al (2018) and Sarpong (2019) is a crucial strategy for the success of international business ventures. Imprecision of forex projections can eliminate the opportunity of profiting from international trade transactions, as most transactions in international business are affected by changes in the exchange rate (Madura, 2011) (Sarpong, 2019) (Mahapatra & Bhaduri, 2019). The process of making forecasting of market indicators, better known as market based-forecasting is usually developed based on spot rates and forward rates.…”

Section: Introductionmentioning

confidence: 99%

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Future Spot Rate: The Implications in Indonesia

Adisetiawan

Bukit²,

Ahmadi³

2020

JIUBJ

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Investors, multinational companies and governments require a rate forecasting to make informed decisions about the hedging of debts and receivables, funding and short-term investments, capital budgeting and long-term financing. The process of making forecasting from market indicators, known as market-based forecasting, is usually developed based on spot rates and forward rates. The current spot rate can be used as forecasting, as the exchange rate reflects the market estimate of the spot rate in a short period of time. The forward rate is used in forecasting, as the exchange rate reflects the market estimate of the spot rate at the end of the forecasting period. Based on the research conducted by Chiang (1986) of the samples used, empirical evidence indicates spot rates and forward rates are significant as predictors of future spots. Empirical evidence suggests that spot rates provide better forecasting results compared to forward rates. The research uses regression models for market-based forecasting methods. The variables used in this study are spot rates, forward rates and future spots. The samples used are from Bank Indonesia for spot rates in January – March 2019 and future spot in April – June 2019, and from Jakarta Futures exchange for forward rates in January – March 2019. The Stochastic and Chow Test models are selected and their use has been evaluated using quality and precise testing measures. Based on the sample period used, empirical evidence suggests that spot rates and forward rates are significant in predicting future spots for EUR, JPY and AUD currencies. Current spot rates provide better forecasting results in predicting Future spot compared to the forward rate. Both the 15Ft"> and 15St"> coefficient are sensitive to new information from the variation of the coefficient and time, it can increase the forecasting of the equation to each currency exchange rate used. The study states that variables from time series should be effectively utilized and utilized in predicting currency exchange rates, as this research demonstrates the absence of dependence on time series Can be concluded that foreign exchange rates in each country follow a pattern that is not stationary. The spot Euro exchange rate turns out to be statistically more accurate with an error rate of 0.004144% forecasting with the value of regression coefficient of Euro exchange rate is a Future Spot = 21.504,88 – 0.341229Spot + 15et+1"> .

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Section: Spot Ratesmentioning

confidence: 99%

Section: Future Spot Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Future Spot Rate: The Implications in Indonesia

Adisetiawan

Bukit²,

Ahmadi³

2020

JIUBJ

View full text Add to dashboard Cite

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“…In addition, the production rate of a country and the barometer of its international attractiveness or competitiveness, portfolio allocation, and management are greatly influenced by ER [4][5][6][7][8]. Thus, volatility in the ER has far-reaching severe consequences on countries, investors, companies, policymakers, and consumers [9,10]. For example, variations in ER make returns on investment undefined, negatively affecting investment decisions.…”

Section: Introductionmentioning

confidence: 99%

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

Adekoya

Nti

Weyori

2021

FinTech

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An accurate prediction of the Exchange Rate (ER) serves as the basis for effective financial management, monetary policies, and long-term strategic decision making worldwide. A stable and competitive ER enables economic diversification. Economists, researchers, and investors have conducted several studies to predict trends and facts that influence the ER’s rise or fall. This paper used the Long Short-Term Memory Networks (LSTM) framework to predict the weekly exchange rate of one Ghanaian Cedis (GH₵) to three different currencies (United States Dollar, British Pound, and Euro), using Google Trends and historical macroeconomic data. We fused past exchange rates, fundamental macroeconomic variables, commodity prices (cocoa, gold, and crude oil) and public search queries (Google Trends) as input parameters. An empirical analysis using publicly available ER data from the Bank of Ghana (BoG) from January 2004 to October 2019 showed satisfactory results. We observed that the proposed LSTM model outperformed the Support Vector Regressor (SVR) and Back-propagation Neural Network (BPNN) models in accuracy and closeness metrics. That is, our LSTM model obtained (MAE = 0.033, MSE = 0.0035, RMSE = 0.0551, R2 = 0.9983, RMSLE = 0.0129 and MAPE = 0.0121) compared with SVR (MAE = 0.05, MAE = 0.005, RMSE = 0.0683, R2 = 0.9973, RMSLE = 0.0191 and MAPE = 0.0241) and BPNN (MAE = 0.04, MAE = 0.0056, RMSE = 0.0688, R2 = 0.9974, RMSLE = 0.0172 and MAPE = 0.0168). Moreover, we observed a strong positive correction (0.98–0.99) between Google Trends on the currency of focus and its exchange rate to the Ghanaian cedis. The study results show the importance of incorporating public search queries from search engines to predict the ER accurately.

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