• Determinants of the Demand for Reinsurance for the U.S. Property-Liability Insurance Industry: Quantile Regression Analysis

Abstract

Unlike previous studies, this study applies two-stage quantile regression (2SQR) to analyze how insurers’ demands for reinsurance are determined. The evidence shows that liquidity and loss development present contradictory influences at the lower and higher reinsurance quantiles. These interesting findings exactly differ from the results reported in previous literature. In addition, even though prior studies predict consistency among quantiles, this study reveals the magnitude of demand for reinsurance impacts, and the significant differences across various reinsurance quantiles. Thus, the evidence proposes that the traditional two-stage least squares regression (2SLS) may be insufficient, or produce a biased explanation on the determinants of insurers’ demand for reinsurance. Additionally, the complementary function of the 2SQR approach on demand for reinsurance analysis is enriched as a whole. For policyholders, policy makers, and/or regulators, it is critical to integrate the empirical implications of the 2SLS and 2SQR approaches while evaluating an insurer’s demand for reinsurance.

Keywords: Demand for Reinsurance, Risk Management, Two-Stage Quantile Regression (2sqr), Two-Stage Least Squares Regression (2sls), Firm-Specific Characteristics