Modeling and Forecasting USD/UGX Volatility through GARCH Family Models: Evidence from Gaussian, T and GED Distributions

Abstract

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 01, 2005 to August 30, 2018. Modeling and forecasting were performed based on Gaussian, Student's t and GED distribution densities to identify 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 and assessed based on symmetric lost functions (RMSE, MAE, MAPE and Thiel's U) and information criteria (AIC, SBC and Loglikelihood). Information criteria offered 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, we recommend PARCH (1,1) and EGARCH (1,1) for modeling and forecasting volatility with preference to GED 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.

Comparative Evaluation of Forecast Accuracies for Arima, Exponential Smoothing and Var

“…On the other hand, EGARCH (1, 2) successfully overcame the leverage effect in the exchange rate returns. Similar studies done by Erkekoglu et al (2020) in Uganda recommend the use of PARCH (1,1) and EGARCH (1,1) in modeling and forecasting volatility. Nanayakkara et al (2014) carried out a study based on Sri Lanka using exchange rates data from January 2007 to November 2011.…”