This study investigates the impact of the COVID-19 pandemic on banking sector profitability in Uganda for the period spanning Q1 2000 to Q1 2021, using the autoregressive distributed lag (ARDL Bound) testing approach to co-integration while controlling for bank specific and macroeconomic determinants of bank profitability. Bank profitability is proxied by return on assets (ROA), return on equity (ROE), and net interest margin (NIM). The study finds that the COVID 19 pandemic has a significant negative effect on bank profitability only in the long run. Generally, the explanatory variables used in the study have short run and long run effects on bank profitability, although the impact is not uniform across the different measures of bank profitability. In the short run, bank profitability is generally negatively and significantly affected by the non-performing loans ratio, liquidity ratio, and market sensitivity risk, while the Treasury Bill interest rate and lending rate have a significant positive effect on bank profitability. In addition, the study finds that bank profitability has a tendency to persist in the short run, although persistence is only moderate, suggesting that the Ugandan banking sector may not have large deviations from a perfectly competitive market structure. In the long run, bank profitability is broadly positively and significantly affected by the non-performing loan ratio;, real GDP, lending rate and Treasury Bill interest rate while market sensitivity risk and the exchange rate significantly and negatively affect bank profitability. Surprisingly, the study finds inflation does not significantly affect bank profitability over both the short- and long-term.
This study investigates the impact of commodity price volatility spillovers on financial sector stability. Specifically, the study investigates the spillover effects between oil and food price volatility and the volatility of a key macroeconomic indicator of importance to financial stability: the nominal Uganda shilling per United States dollar (UGX/USD) exchange rate. Volatility spillover is examined using the Generalized Vector Autoregressive (GVAR) approach and Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) techniques, namely the dynamic conditional correlation (DCC), constant conditional correlation (CCC), and varying conditional correlation (VCC) models. Overall, the results of both the GVAR and MGARCH techniques indicate low levels of volatility spillover and market interconnectedness except during crisis periods, at which point cross-market volatility spillovers and market interconnectedness sharply and markedly increased. Specifically, the results of the MGARCH analysis show that the DCC model produces the best results. The obtained results point to an amplification of dynamic conditional correlations during and after the global financial crisis (GFC), suggesting an increase in volatility spillovers and interdependence between these markets following the global financial crisis. This is also confirmed by the results of the total spillover index based on the GVAR analysis, which shows low but time-varying volatility spillover that intensified during periods of high uncertainty and market crises, particularly during the global financial crisis and sovereign debt crisis periods.
This paper examines the effects of macroeconomic policy and regulatory environment on mobile money usage. Specifically, we develop an autoregressive distributed lag model to investigate the effect of key macroeconomic variables and mobile money tax on mobile money usage in Uganda. Using monthly data spanning the period March 2009 to September 2020, we find that in the short run, mobile money usage is positively affected by inflation while financial innovation, exchange rate, interest rates and mobile money tax negatively affect mobile money usage in Uganda. In the long run, mobile money usage is positively affected by economic activity, inflation and the COVID-19 pandemic crisis while mobile money customer balances, interest rate, exchange rate, financial innovation and mobile money tax negatively affect mobile money usage.
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