AFRIMA Model Forecast Performance: An Empirical Study using Naira-Yuan Exchange Rate

In financial time series and econometrics, some macroeconomic variables exhibit long memory features that may not be best described using short memory models like ARIMA. This paper, however, is structured to compare different fractional integration in AFRIMA forecast performance for the Naira-Yuan exchange rate. The empirical monthly data set used covered the period from January 1981 to December 2022. Fractional integration test are based on the ADF unit root test and the auxiliary autoregressive order three (AAR(3)) order of integration test. Model estimation is support by the Marquart algorithm for calculating least squares estimates and performance comparison is based on the Amaefula forecast criterion (AFC). The result specified that AFRIMA (1, d, 1) where I(d = 0.07891) is more appropriate and has the best forecast performance compared to others. The result also reveals that AFRIMA model yield better and more precise forecasts when fractional integration is closer to zero that is, I(d→0) than when I(d→½). Therefore, AFRIMA models can be useful in studying exchange rate dynamics for risk-averse and risk incline in times of investment and profitability in the long-run.