|dc.description.abstract||This research examines three different but related aspects of the stock market return volatility. The first study quantifies the effects of political instability and democracy on stock market return volatility. It uses a system-generalized method of moments (sys-GMM) model and a system of equations model to estimate the relationship between political risk and market volatility for a sample of 54 developing and high-income countries for the period 1995-2014. The dynamic panel data analysis using the sys-GMM model reveals that political instability causes the stock market to be volatile, yet the evidence on the effects of democracy on volatility cannot be established. This suggests that, though democracy does not affect the volatility directly, there may be some indirect effects of democracy on stock volatility. The second set of econometric estimates using the system of equations differentiates the direct and indirect impacts of democracy on stock market return volatility. This study shows that political stability has a direct impact on reducing stock market volatility, while the degree of democracy has an indirect impact on volatility, transmitted through the channels of political stability. In addition, macroeconomic uncertainty, triggered by high inflation and low economic growth, causes uncertainty in the stock market. The institutional reforms that target the reduction of uncertainty on policy-formulation in the country will contribute to stabilizing financial markets.
The second study, forecasting stock market return volatility, assesses the forecasting performances of a set of statistical models on the daily data for selected Asian emerging stock markets, namely India, Malaysia, Pakistan, Singapore, Sri Lanka, and Thailand. These forecasting models, including generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and threshold GARCH (TGARCH) models, were used to predict the volatility using out-of-sample and in-sample forecasting methods. This study finds that, in terms of the in-sample forecasting method, asymmetric GARCH models had better performance than the symmetric GARCH models. Conversely, in terms of the out-of-sample forecasting method, the symmetric GARCH models perform better than the asymmetric GARCH models.
The third study investigates another different perspective of stock market return volatility: that market volatility plays a crucial role in shaping income inequality and income distribution within a country. The study used an instrumental variable (IV)-GMM estimation procedure, with the Lewbel (2012) identification approach for heteroskedastic error models, on a panel of 47 high-income and developing countries for non-overlapping five-year average data. One of the key findings reveals that stock market return volatility worsens the income inequality within a country. Stock market return volatility negatively affects the average income of the poorest 10 percent (decile 1) of the income distribution. Yet, such a significant relationship was not found with the top decile (decile 10). The income inequality tends to be reduced with the higher degree of trade openness and higher level of per-capita gross domestic product (GDP) of the economy.
In summary, this research reveals three main findings: first, political instability seems to appear as a major cause of stock market return volatility; second, the assessment of the predictive ability of different forecasting models reveals that the simple GARCH model provides a strong predictive performance; third, widening income inequality can be seen to be an emerging consequence of market volatility.||