Advanced Techniques for Modeling Terrorism Risk

FALL 2002 THE JOURNAL OF RISK FINANCE 15 T he attack of September 11, 2001, showed that terrorism is capable of inflicting damages ($40bn+) and loss of life (3,000+) that are multiples of the worst U.S. natural disasters (Hurricane Andrew at $20bn+ and 40-60 lives; Northridge earthquake at $12.5bn and approximately 25 lives) (Bassett and Schroeder [1998]; Mooney [2001]; Pawlowski [2001]). In the wake of this unprecedented disaster, insurers and reinsurers have been excluding terrorism risk from their offerings, with grave consequences for commercial property owners and lenders (Mooney [2001]). Most insurance analysts and actuaries would agree with Munich Re’s Christian Kluge: “There is no mathematical model for terrorism” (Fromme [2001]). But the need for one is clear. Terrorism risk shares features with other forms of catastrophe risk, including a time series of historical events, yet goes beyond them with an extra layer of impenetrability. Defensive studies of terrorism risk resemble risk analyses of complex engineering systems (nuclear power plants, satellite launches, etc.). A particular scenario can be analyzed in terms of the probability of failure of critical subsystems. However, unlike natural disasters, it features human intelligence, and unlike industrial disasters it features human intent. To quantify the risk, much like solving the celebrated “three doors” puzzle (see Appendix A), probability is not enough. Methods from operations research, including game theory and search theory, as well as certain specialized areas of statistics, may well be needed to construct an adequate modeling framework.