Ito Calculus-Machine Learning Projection of Forward US Dollar-Ghana Cedi Rates

Abstract

In this paper the fundamental solution to CMSVJD is implemented to simulate USD/GHS forward exchange rates under the condition of jumps. CMSVJD was generalized in this study to include conditions of ‘no jump’ and ‘reduced parameters’. The model captures both stochastic volatility and jumps. Stochastic calculus modeling and machine learning interface is the answer to speculators and chartists driving prices in the Ghana interbank market (a frontier market). Codes written in R were used for simulation and plot of results. A 5-fold cross validation was used to create the test sample data, as proxy for forward rates required to validate the performance of the model. The resultant machine yields a predictive result of forward USD/GHS rates with a mean squared error of 0.169% and 0.5%.