The Impacts of Terrorist Events on Stock Market Volatility

Acts of terrorism have increased markedly in recent decades. Social analyses have found terror-related events to be associated with nationwide increases in fear, panic, and uncertainty amongst individuals and businesses who play a major role in the stock market. The current paper intends to contribute to the established literature by quantitatively examining the impacts of terrorist attacks on financial market volatility by conducting an in-depth case study of the Sri Lankan stock market during and after the civil war (1986-2017). Sri Lanka serves as an excellent natural experiment for the current study due to the sustained length and severity of the terrorist attacks between 1983 and 2009. Five forms of terror variables: (i) cumulative number of deaths; (ii) cumulative number of victims injured; (iii) number of fatalities; (iv) number of injured, and (v) a terror dummy variable were utilized in modeling the stock market volatility. This paper uses autoregressive conditionally heteroscedastic (ARCH), and generalized autoregressive conditionally heteroscedastic (GARCH) methods to model the stock market volatility. The method is consistent with the assumption that the innovations from stock returns models contain a fixed unconditional variance, but the conditional variance varies. The findings of this paper suggest that terror attacks, in general, have exerted a significant effect on stock market volatility in Sri Lanka during the conflict period. Surprisingly, extrinsic factors of terror-related events such as the number of people injured or killed due to terrorist attacks were not found to be statistically significant jointly or separately in the conditional volatility equation, thus making it difficult to form a clear interaction among the variables. Investors seem to concern themselves more about the risk of instability which manifests as a result of terror-related events rather than the human cost of such events. The findings of this paper may be useful in improving the accuracy of future volatility prediction models. They could also be relevant in evaluating military sector projects that aim to boost investor confidence.