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

Hatice Erkekoğlu ,Adire Simon Deng,Aweng Peter Majok Garang

doi:10.32479/ijefi.9016

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 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

Highlights

International financial cash flows tend to be hugely affected by uncertainties due to fluctuations in key economic markets such as foreign exchange and stock markets, which results into the decline of exports and imports, which in turn affect welfare as suggested by (Twamugize et al, 2017)
We model volatility using generalized ARCH (GARCH) family models by taking into consideration the assumptions on distribution densities and making forecasts to determine appropriate models
Since the leverage coefficient (λ) was positive and significant in Power GARCH (PARCH) (1,1), it could be established that negative past innovations influenced volatility than positive values of similar magnitudes

Summary

Introduction

International financial cash flows tend to be hugely affected by uncertainties due to fluctuations in key economic markets such as foreign exchange and stock markets, which results into the decline of exports and imports, which in turn affect welfare as suggested by (Twamugize et al, 2017). Influential attempts aimed at modeling volatility were introduced into literature through the seminal work of Engle (1982) in which he proposed conditionalizing variance in an autoregressive heteroscedastic process by introducing the autoregressive conditional heteroscedastic (ARCH) model. This model, posed challenges due to huge lag specifications. The literature on these models has grown to capture stylized facts of financial

Methods

Results

Conclusion