This article applies a bootstrap rolling-window causality test to assess the causal relationship between economic policy uncertainty (EPU) and stock returns in China and India. Empirical literature examining causality between two time series may suffer from inaccurate results when the underlying full-sample time series have structural changes. However, the bootstrap rolling-window approach enables us to identify possible time-varying causalities between time series based on sub-sample data. Using a twenty-four-months rolling window over the period 1995:02 to 2013:02 in China and 2003:02–2013:02 in India, we do find that there are bidirectional causal relationships between EPU and stock returns in several sub-periods rather than in the whole sample period. However, the association between EPU and stock returns is, in general, weak for these two emerging countries. Our findings have important implications for policy makers and investors.

In this article, we analyze the role of country-specific and global geopolitical risks (GPRs) on the returns and volatility of 18 emerging market economies over the monthly period of 1998:11 to 2017:06. For our purpose, we use a panel Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approach, which offers substantial efficiency gains in estimating the conditional variance and covariance processes by accounting for interdependencies and heterogeneity across economies, unlikein a time series-based GARCH model. We find that, while country-specific GPRs do not have an impact on stock returns, and the positive effect on equity market volatility is statistically weak. But when we consider a broad measure of global GPR, though there is still no significant effect on returns, the impact on volatility is both economically and statistically stronger than that obtained under the country-specific GPRs, thus highlighting the dominance of global rather than domestic shocks.

The aim of this study is to understand the effect of the recent novel coronavirus pandemic on investor herding behavior in global stock markets. Utilizing a daily newspaper-based index of financial uncertainty associated with infectious diseases, we examine the association between pandemic-induced market uncertainty and herding behavior in a set of 49 global stock markets. More specifically, we study the pattern of cross-sectional market behavior and examine whether the pandemic-induced uncertainty drives directional similarity across the global stock markets that cannot be explained by the standard asset pricing models. Utilizing a time-varying variation of the static herding model, we first identify periods during which herding is detected. We then employ probit models to examine the possible association between pandemic-induced uncertainty and the formation of herding. Our findings show a strong association between herd formation in stock markets and COVID-19 induced market uncertainty. The herding effect of COVID-19 induced market uncertainty is particularly strong for emerging stock markets as well as European PIIGS stock markets that include some of the hardest hit economies in Europe by the pandemic. The findings establish a direct link between the recent pandemic and herd formation among market participants in global financial markets. Considering the evidence that herding behavior can drive security prices away from equilibrium values supported by fundamentals and further contribute to price fluctuations in financial markets, our findings have significant implications for policy makers and investors in their efforts to monitor investor sentiment and mitigate mis-valuations that might occur as a result. Furthermore, the evidence on the behavioral pattern of stock investors in relation to infectious diseases uncertainty can be useful in studying price discovery in stock markets and might help market participants in forming hedging strategies to mitigate downside risk in their investment portfolios.

We examine the predictive power of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) for oil-market volatility. Using the heterogeneous autoregressive realized volatility (HAR-RV) model, we document a positive effect of the EMVID index on the realized volatility of crude oil prices at the highest level of statistical significance, within-sample. Importantly, we show that incorporating EMVID into a forecasting setting significantly improves the forecast accuracy of oil realized volatility at short-, medium-, and long-run horizons. Our findings comprise important implications for investors and risk managers during the unprecedented episode of high uncertainty resulting from the COVID-19 pandemic.