Market Expectations Following Catastrophes: An Examination of Insurance Broker Returns

INTRODUCTION The commercial insurance industry relies heavily on financial capital to maintain equilibrium. Large disasters reduce available capital, creating uncertainty in the market. To recoup lost capital, insurers may raise external funds, increase prices, and/or restrict coverage, but they also must consider how policyholders, competitors, investors, and regulators may react. Insurance buyers may face rate increases, impaired insurers, and existing losses as they make decisions regarding future insurance purchases. Considering all these factors, how is the commercial insurance market equilibrium expected to change following a major disaster? Many existing theories suggest that insurance supply will contract after a capital shock, but few explicitly consider the response of commercial demand. Following seminal work by Winter (1994) and Gron (1994), Cagle and Harrington (1995) develop a model of insurer profits, ultimately showing that a shock's effect on price depends on the elasticity of demand relative to the elasticity of supply. The authors note that insurance guaranty funds, compulsory insurance requirements, and the lack of substitutes for insurance make it likely that demand is less elastic than supply, concluding that prices will increase following a capital shock. (1) The measurement of corporate insurance demand elasticities has not been widely studied--the only empirical article estimating corporate insurance demand elasticities that we are aware of is a study by Michel-Kerjan, Raschky, and Kunreuther (Forthcoming). The researchers use proprietary purchasing data to estimate the price elasticity of demand for corporate property and terrorism insurance and find that firms were relatively price inelastic for both types of coverage. (2) The objective of our article is to contribute to this nascent literature by examining the expected effect of a major disaster on commercial insurance market equilibrium. Our approach is innovative in several ways. First, we use an event study methodology to examine stock returns, which isolates the effect of a catastrophe from other factors that may impact the insurance market. In other words, rather than examining the realized revenue changes in the quarter following the disaster (which would be influenced by interest rates, investment returns, inflation, and other losses), we study the stock market's immediate reaction in the days following the 43 largest insured-loss catastrophes since 1970. This captures any new expected change in insurance premium revenue as a direct result of the disaster. Second, rather than examining stock returns for insurers, we examine returns for insurance brokers. Net increases in premium revenue benefit insurers, but that benefit may be offset by claim payments following a disaster. Prior catastrophe event studies focusing on insurers find that insurers generally experience negative abnormal returns as a result of their expected loss payments (Lamb, 1995; Cummins and Lewis, 2003; Doherty, Lamm-Tennant, and Starks, 2003). Insurance brokers, on the other hand, derive a substantial portion of their revenues from commissions on premiums paid, so broker revenues may serve as a proxy for insurer revenues. Unlike insurers, however, insurance brokers have little (if any) exposure to policyholder claims. Hence, expected changes in premium revenue resulting from a catastrophe more directly affect broker profits than insurer profits. We examine broker stock returns to evaluate the expected change in broker profits. The results of these returns will thus provide information on market expectation of insurance industry revenue immediately after a catastrophe's shock to financial capital. Finally, with the results of our event studies, we conduct a cross-sectional regression to examine factors associated with the size of the cumulative abnormal returns (CARs) for brokers. Our primary variable of interest is the shock to insurer capital as measured by the size of the insured loss. …