Investors, Policy makers, Governments etc. are all consumers of exchange rates data and thus exchange rate volatility is of great interest to them. Modeling foreign exchange (FOREX) rates is one of the most challenging research areas in modern time series prediction. Neural Network (NNs) are an alternative powerful data modeling tool that has ability to capture and represent complex input/output relationships.
This study describes application of neural networks in modeling of the Kenyan currency (KES) exchange rates volatility against four foreign currencies namely; USA dollar (USD), European currency (EUR), Great Britain Pound (GBP) and Japanese Yen (JPY) foreign currencies. The general objective of the proposed study is to model the Kenyan exchange rate volatility and confirm applicability of neural network model in the forecasting of foreign exchange rates volatility. In our case the Multilayer Perceptron (MLP) neural networks with back-propagation learning algorithm will be employed. The specific objectives of the study is to build the neural network for the Kenyan exchange rate volatility and examine the properties of the network, finally to forecast the volatility against four other major currencies. The proposed study will use secondary data of the mean daily exchange rates between the major currencies quoted against the Kenyan shilling. The data will be acquired from the central bank of Kenya's (CBK) website collected for ten years of trading period between the years 2005 to 2017. The data will be analyzed using both descriptive and inferential statistics, with the aid of R's neuralnet package. A number of performance metrics will be employed to evaluate the model. Conclusion and recommendations will be made at the end of the study.