This paper analyses the causal relationship between Foreign Portfolio Investment (FPI), Equities Market Volatility, Exchange Rate and Inflation in Zimbabwe using a monthly time series data between October 2018 and November 2021. The granger causality model was used to present the link between the variables, and EGARCH was used to account for volatility and asymmetric effects on the variables. To incorporate innovations and responses into the Granger model, impulse response functions were used. Links between exchange rate and foreign portfolio investments were found. This only suggests that exchange rate volatility will vary when overseas investors purchase and sell financial securities on the Zimbabwe Stock Exchange (ZSE). In contrast, foreign investors sell local financial securities when local stock market returns are negative, leading to a significant outflow of foreign portfolio investment thereby reducing demand for currency. A significant causal relationship was found between the volatility of the exchange rate and stock market returns. It is assumed that stock market returns, and foreign portfolio investments are caused by fluctuating currency rates. The relationship between exchange rate and ZSE returns, and inflation was found based on Granger causality. This implies that stocks are not suitable for long-term investments that compensate investors for their diminished purchasing power. Policy makers should advise the Zimbabwe Stock Exchange to recommend a reduction in capital gains tax and withholding tax and this encourages investors to hold local equities for a long time.

This study aims to the rise in global economic integration is due to an expansion in volatility spillovers. Therefore, it is extraordinarily necessary to analyze the volatility spillovers for growing and developed international locations through the use of portfolio funding and danger management. This lookup investigates the Volatility Spillovers of Sharia Index on 6 ASEAN international locations all through the Covid-19 Pandemic the usage of the EGARCH model. Data have been received from 5 international locations with enormous volatility spillovers, particularly Indonesia, Malaysia, Singapore, Thailand, and Vietnam, to decide the reciprocal relationship of the inventory index in ASEAN as properly as the route of volatility movements. The result confirmed that this lookup is necessary for ASEAN traders besides for the Philippines. Furthermore, this lookup has sturdy sensible significance due to the fact the correct prediction of the volatility spillovers in worldwide fairness markets is quintessential for decreasing portfolio risk.

In this study, the backpropagation neural network (BPNN) is tested for the ability to forecast the daily volatility of two stock market indices from the Middle East and North Africa (MENA) region using volume; namely Morocco and Saudi Arabia. Volatility series were estimated using the Exponential Auto-Regressive Conditional Heteroskedasticity (EGARCH) model. The simulation results show that trading volume helps improving the forecasting accuracy of BPNN in Morocco but not in Saudi Arabia. As a result, volume represents valuable information flow to be used in the modeling and prediction of volatility in Morocco. In addition, it is found that BPNN overpredicts volatility during high volatile periods. This finding is important in financial applications such as asset allocation and derivatives pricing.