Adire Simon Deng’s scientific contributions

While various linear and nonlinear forecasting models exist, multivariate methods like VAR, Exponential smoothing, and Box-Jenkins' ARIMA methodology constitute the widely used methods in time series. This paper employs series of Turkish private consumption, exports and GDP data ranging between 1998: Q1 and 2017: Q4 to analyze the forecast performance of the three models using measures of accuracy such as RMSE, MAE, MAPE, Theil's & . Seasonal decomposition and ADF unit root tests were performed to obtain new deseasonalized series and stationarity, respectively. Results offer preference for the use of ARIMA in forecasting, having performed better than VAR and exponential smoothing in all scenarios. Additionally, VAR model provided better forecast accuracy than exponential smoothing on all measures of accuracy except on Thiel's whose VAR values were not computed. Cautionary use of ARIMA for forecasting is recommended. Keywords: Forecast Evaluation, ARIMA, Exponential Smoothing, VAR JEL Classifications: C1, E00, C51 DOI: https://doi.org/10.32479/ijefi.9020

Symmetric and asymmetric GARCH models-GARCH (1,1); PARCH(1;1); EGARCH(1,1,); TARCH(1,1) and IGARCH(1,1)- were used to examine stylized facts of daily USD/UGX return series from September 1st, 2005 to August 30th, 2018. Modeling and forecasting were performed based on Gaussian, Student’s t and GED distribution densities with a view to identifying the best distribution for examining stylized facts about the volatility of returns. Initial tests of heteroscedasticity (ARCH-LM), autocorrelation and stationarity were carried out to establish specific data requirements before modeling. Results for conditional variance indicated the presence of significant asymmetries, volatility clustering, leptokurtic distribution, and leverage effects. Effectively, PARCH (1,1) under GED distribution provided highly significant results free from serial correlation and ARCH effects, thus revealing the asymmetric responsiveness and persistence to shocks. Forecasting was performed across distributions & assessed based on symmetric lost functions (RMSE, MAE, MAPE & Thiel’s U) and information criteria (AIC, SBC & Loglikelihood). The information criteria offered a preference for EGARCH (1,1) under GED distribution while symmetric lost functions provided very competitive choices with very slight precedence for GARCH (1,1) and EGARCH (1,1) under GED distribution. Following these results, it’s recommended that PARCH (1,1) and EGARCH (1,1) be respectively preferred for modeling and forecasting volatility with GED as the choice distribution. Given the asymmetric responsiveness and persistence of conditional variance, macroeconomic & fiscal adjustments in addition to stabilization of the internal political environment are advised for Uganda. Keywords: Forecasting volatility, GARCH family Models, Probability Distribution Density, Forecast accuracy.JEL Classifications: C58, C53, G17, F31DOI: https://doi.org/10.32479/ijefi.9016