SOLVENCY ANALYSIS AND PREDICTION IN PROPERTY–CASUALTY INSURANCE: INCORPORATING ECONOMIC AND MARKET PREDICTORS

Li Zhang and Norma Nielson

Journal of Risk & Insurance, 2015, vol. 82, issue 1, 97-124

Abstract: type="main" xml:lang="en">

This article extends the insolvency prediction literature by incorporating macroeconomic conditions and state-specific factors. The models achieve greater generalizability and predictive accuracy than earlier research while giving fewer false positives. At the firm level, we find insurers with less diversified business, sufficient cash flow, high return on equity, lower leverage, fewer failed Insurance Regulatory Information System ratio tests, and membership in a larger group are less likely to become insolvent. Our findings support the argument that insolvency likelihood increases for insurers domiciled in states with stricter solvency supervision and/or states with less favorable insurance market conditions, and during soft markets; insolvency risk is negatively related to the slope of the yield curve. Our findings also imply that insurers respond efficiently to changes in such market factors as market return, inflation, and catastrophic losses.

Date: 2015
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