Forecasting Foreign Exchange Rates In Kenya Using Time Series: A Case Of Usd/Kes Exchange Rates

ABSTRACT

In October 1993, Kenya adopted a floating exchange rate system where the exchange

rates are determined by forces of demand and supply for the local currency. Exchange rate

forecasts are necessary to evaluate the foreign denominated cash flows involved in international

transactions. Therefore exchange rate forecasting is important to evaluate the benefits

and risks attached to the international business environment. This study therefore

sought to fit a Seasonal Autoregressive Intergrated Moving Average Model(SARIMA)(p, d,

q)(P,D,Q)[12] to United States Dollar vs Kenya Shilling exchange rate since it is the

most dominant exchange rate in Kenya. The secondary monthly data from January

1993 to March 2019 from Central Bank of Kenya official website was divided into two

parts namely the in-sample data and the out-sample data. The in-sample data was

used to fit the model while the out-sample was used to validate the model. Seasonal

Mann-Kendall test established that there was seasonal trend. A first regular difference

was used to stationarize the series since the ADF test established it was not stationary.

Autoregressive Intergrated Moving Average (ARIMA) and Seasonal Autoregressive Intergrated

Moving Average (SARIMA) models were fitted in the data. ARIMA(1, 1, 0) and

SARIMA(1, 1, 0)(0, 0, 2)[12] were found to be the best models on the basis of Bayesian Information

Criterion(BIC) and Akaike’s Information Criterion(AIC). In the short run i.e 3

months, the Seasonal Autoregressive Intergrated Moving Average had the least Mean Absolute

Error(MAE), Mean Absolute Percentage Error(MAPE) and Root Mean Squared

Error(RMSE) values of 0.1651, 0.1636 and 0.2037 respectively. This study therefore

recommends the integration of the Seasonal Autoregressive Intergrated Moving Average

Model in forecasting United States Dollar vs Kenya Shilling exchange rate in Kenya in

the short run.