Daily Return Behavior of the Insurance Industry: The Case of Contingent Commission

1. Daily Return Behavior of the Insurance Industry: The Case of Contingent Commission Jiang Cheng Elyas Elyasiani Tzuting Lin Temple University 2007 ARIA Annual Meeting, Quebec City

2. Motivation • New York Attorney General Eliot Spitzer filed a civil suit in the State Supreme Court against Marsh & McLennan Cos. for “bid-rigging” and inappropriate use of “contingent commissions” on Oct. 14, 2004. • We test the market reaction on insurance brokers and property-liability and life-health-accident insurers from the civil action suit using event study methodology within a GARCH framework. • The bid-rigging event provides a good opportunity to test the effects of contingent commissions on the insurance industry.

3. Findings • The event generated negative effects both within the brokerage sector and for individual brokerage firms, suggesting that the contagion effect dominates the competitive effect. • The inter-sectoral information spillover effects across the brokerage, property-liability, and life-health sub-sectors of the insurance industry are also significant and mostly negative. • Our results support the information-based hypothesis against the pure-panic contagion effect as the size of the impact due to the event is highly correlated with firm characteristics. • ARCH/GARCH effects are significant for both the sectoral portfolios and about half of individual brokers and property-liability insurers.

4. Insurance Marketing systems and Contingent Commission • Direct Marketing Insurers (DMIs) : direct writer + exclusive agents • Insurers with Independent Intermediaries (IIIs) : independent agents + brokers • Contingent Commission pros: alignment of interests between insurers and brokers cons: the potential conflict of interest for brokers and against the buyers

5. Literature • Event Study: the effects of California’s Proposition 103 (Fields et al., 1990; Szewczyk and Varma, 1990; Shelor and Cross, 1990; Grace et al., 1995; and Brockett et al., 1999), the 1989 California earthquake (Shelor et al., 1992), trouble in investment portfolio of First Executive and Travelers (Fenn and Cole, 1994), Hurricane Andrew (Lamb, 1995; Angbazo and Narayanan, 1996), property-liability insurance market pullout (McNamara et al., 1997), the terrorist attacks of September 11, 2001 (Cummins and Lewis, 2003), the European Union Insurance Directives (Campbell et al., 2003), and the impact of operational loss events in the U.S. banking and insurance industries (Cummins et al., 2006a, 2006b). • Contingent commission (Cummins and Doherty, 2006; Kleffner and Regan, 2007). • Stock return data often exhibit GARCH properties (Engle, 1982; Bollerslev, 1987; Akgiray, 1989; Lamoureux and Lastrapes 1990).

7. Methodology • GARCH (1, 1) model • Determinants of Abnormal Returns

8. Data • The SIC codes used are: 6331 for property-liability, 6311 for life, and 6320-6321 for health and accident insurers, and 6411 for the broker companies. • 74 property-liability insurers (excluding AIG, ACE, and Hartford), 40 life-health-accident insurers, and 10 insurance brokers (excluding MMC). • The market return is measured using the CRSP equally weighted index. • The property-liability insurers’ financial data is obtained from the Best’s Key Rating Guide and A.M. Best’s Aggregates and Averages.

15. Comments and Suggestions? Thank you!