The purpose of this paper is to examine the fiscal consolidation impact on government debt in South Africa (SA) looking at both structural and cyclical effects. The paper employs the Structural Vector Autoregression (SVAR) using time-series data from 1990 to 2020 in South Africa. The key contribution of the paper is it with a focus on the effect of fiscal consolidation as well as investigation of the structural and cyclical component effect of government expenditure cut as well as a tax increase in a developing economy like South Africa. We found that government debt falls as of the result of fiscal consolidation achieved through government expenditure cut. The fiscal consolidation of tax increases is better than based on government expenditure cut. The cyclical component of government expenditure increases domestic government debt. This is also found in the structural government expenditure results in an increase in domestic government debt.
The objective of this study is to forecast the trend of inflation in Bangladesh by utilizing past inflation data. To achieve this objective, we employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) model which is an extension of the Autoregressive Integrated Moving Average (ARIMA) model. Monthly inflation data used for forecasting were derived from the Consumer Price Index (CPI) data obtained from the International Monetary Fund (IMF) database, covering the period from January 2010 to January 2023. Our analysis reveals that the SARIMA (2,0,0)×(1,0,1)12 model is the most appropriate fit. Based on this finding, we predicted the inflation trend in Bangladesh from February 2023 to December 2024. A comparison of our predicted values with the actual values indicates a high degree of correlation between the two. Although a few discrepancies were observed, they did not undermine our prediction since the parameters of the model lay within the 95% confidence interval.
ETFs have gained increasing popularity due to their numerous benefits, including their higher liquidity relative to their counterparts. However, the influence of this increasing attention on their liquidity remains unexplored. Therefore, this study investigates the effect of investor attention on ETF liquidity. To achieve this objective, 80 South African ETFs are examined from January 2018 till December 2022 using a panel regression approach. The findings of this study suggest that an increase in investor attention increases the price impact but reduces the cost of trading, ultimately, leading to an improvement in ETF liquidity. Further analysis reveals that investor attention has a greater impact on ETFs tracking domestic benchmarks, and impacts only ETFs tracking equities, bonds, and property. The analyses also reveal that the effect of investor attention is only significant in the short-run and is eliminated in the long-run, and these effects have been intensified by the COVID-19 pandemic. Global investor attention, however, has an opposing effect on ETF liquidity. These findings are important for investors trading in ETF markets and regulators controlling these markets.
Many studies have investigated the impact of expectations on the exchange rates. However, it remains a challenge linking the exchange rates to its fundamentals. This study seeks to determine the impact of expectations of future income on the exchange rates behaviour. In this study, we employ the Bayesian VAR method. The study finds that the expectations of income have effects on the exchange rate behaviour. Furthermore, the exchange rates behaviour is asymmetric.
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