Cross-Industry Consequences of Terrorism

Given the growing importance of terrorism in today's world, it is essential to understand its impact on the global economy. This paper examines the case of Israel to consider the impact of this phenomenon on the nation's economy. Specifically, it will aim to determine whether the Israeli financial market is sensitive to terror attack, how sensitive are the various sectors of the Israeli financial markets and whether sectors react differently from one another.This study considered various financial indices to assess the impact of the amount of fatal and non-fatal terrorism-related daily casualties and the amounts of daily cumulated terror events on industries' unit-root free daily returns. In this time series analysis, the variables were run through an Orthogonalisation process to prevent multicolinearity and the time series filtered to remove economic and global trends. The statistical analysis methods comprise of Multi-Variables Linear Regression, Seemingly Unrelated Regression and Spearman ranking test.The results of this study reveal that the Israeli financial market and all its sectors do suffer from terrorism (-25 percent cumulated loss for TA100). Industries dependent on domestic demand tend to react stronger to terror attacks than those based on exports with the exception of the housing industry. From a statistical point of view, the hypothesis of similar reaction of all sectors of the Israeli economy could not be rejected. The results of this study apply to a vast array of countries thanks to Israel's level of development and free market. An interview was also done with an expert who confirmed most of the theoretical results.The main weaknesses of the study are that the orthogonalisation process biased the weight of the terror data variable and the time series/daily return perspective does not match a certain long-term reality of terrorism. The majority of results match those of previous studies and complete the perspective on the consequences of terrorist conflicts. The process of filtering the time series and variables to remove undesirable data employed in this study is to the author's knowledge novel to this specific area of academic research