Open AccessModeling US Dollar and Nigerian Naira Exchange Rates During COVID-19 Pandemic Period: Identification of a High-performance Model for New Applications
Author(s) -
Chukwudi Paul Obite,
Ugochinyere Ihuoma Nwosu,
Desmond Chekwube Bartholomew
Publication year - 2021
Publication title -
journal of mathematics and statistics studies
Language(s) - English
Resource type - Journals
ISSN - 2709-4200
DOI - 10.32996/jmss.2021.2.1.5
Subject(s) - autoregressive integrated moving average , test set , artificial neural network , computer science , data set , econometrics , set (abstract data type) , autoregressive model , exchange rate , artificial intelligence , economics , machine learning , time series , finance , programming language
This study modeled the US Dollar and Nigerian Naira exchange rates during COVID-19 pandemic period using a classical statistical method – Autoregressive Integrated Moving Average (ARIMA) – and two machine learning methods – Artificial Neural Network (ANN) and Random Forest (RF). The data were divided into two sets namely: the training set and the test set. The training set was used to obtain the parameters of the model, and the performance of the estimated model was validated on the test set that served as new data. Though the ARIMA and random forest performed slightly better than the neural network in the training set, their performance in the test set was poor. The neural network with 5 nodes in the input layer, 5 nodes in the hidden layer and 1 node in the output layer (ANN (5,5,1)) performed better on the new data set (test set) and is chosen as the best model to forecast for future USD to NGN exchange rate. The information from the high-performance model (ANN (5, 5, 1)) for modeling the USD to NGN exchange rate will assist econometric trading of the currencies and offer both speculative and precautionary assistance to individuals, households, firms and nations who use the currencies locally and for international trade.