Property-casualty Insurance Research Articles

How do underwriting and investment activities affect P&C insurers’ capital adjustments? Evidence from Canada

AbstractUsing a sample of 83 Canadian property-casualty insurance companies from 1996 to 2010, we examine the impact of underwriting and investment choices on the insurers’ capital level and adjustment speed. An aggressive investment policy (risky equity investments) and a heavy reliance on reinsurance underwriting activities have the opposite effect on insurers’ capital level, though both lead to a slower capital adjustment speed. Meanwhile, insurers reshuffling their underwriting and investments significantly change their capital. With an integrated framework that considers underwriting cycles and regulatory pressure, insurers are slower in their capital adjustments in hard markets of the underwriting cycle, and higher regulatory pressure for an insurer moderates the positive relationship between capital level and adjustment speed.

Study on the Development of Property Insurance Industry Based on ARIMA and CGDAM-WRIR Models

This study aims to address the climate change challenges faced by the property-casualty insurance industry by using the ARIMA model to assess the potential impacts of extreme weather events on the insurance industry and the CGDAM-WRIR model to analyze the regional impacts of tornadoes and the impacts of climate change on communities. With these models, we provide accurate risk management and underwriting strategy adjustment recommendations. Future research will further explore complex models, overcome limitations, integrate multi-source data, and develop real-time predictive models to help the insurance industry better manage climate change uncertainty, safeguard property owner interests, and ensure market sustainability and resilience.

Digitalisation and cream skimming adverse selection in the property-casualty insurance industry: evidence from China

Digitalisation and cream skimming adverse selection in the property-casualty insurance industry: evidence from China

US Property Casualty Insurers’ Responses to Mega Natural Disasters

US Property Casualty Insurers’ Responses to Mega Natural Disasters

Modeling underwriting risk: A copula regression analysis on U.S. property-casualty insurance byline loss ratios

This article evaluates underwriting risks for U.S. property-casualty insurance company using a by-line multivariate framework. We propose a regression system with copula structure to estimate the dynamics and dependences of by-line loss ratio changes. The dynamics are characterized by autoregressive, macroeconomic, and line-specific variables. The dependences are characterized by symmetric, asymmetric, and vine copulas to manifest the contemporaneous risk. Both positive and negative dependence of loss ratio changes are found, and the lines of business contribute distinct diversification effects from the Risk-based Capital (RBC) R5 formula suggested. Based on empirical studies of industry-wide data, we confirm that the choice of copula structure and marginal estimation method affects the model's performance in the stress testing of catastrophic events. The proposed approach can be regarded as an internal model calculating the diversification benefits across business lines, based on a statistical hypothesis in contrast to a regulatory base. These findings help insurers and regulators to evaluate their capital requirements more precisely and reduce liability-side risk.

Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves

The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the Sarmanov family of multivariate distributions to the actuarial literature. In fact, we study rank-based methods using the Sarmanov distribution to adequately estimate the loss reserves and properly capture the dependence between lines of business. An inadequate choice of the dependence structure may negatively impact the estimation of the marginals and, hence, the reserve. Thus, we propose a two-stage inference strategy in this research to address this, while taking advantage of the flexibility of the Sarmanov distribution. We show that this strategy leads to a more robust estimation, and better captures the dependence between the risks. We also show that it generates smaller risk capital and a better diversification benefit. We extend the model to the multivariate case with more than two lines of business. To illustrate and validate our methods, we use three different sets of real data from both a major US property–casualty insurer and a large Canadian insurance company.

Risk taking, performance, and resilience to the COVID-19 pandemic: Evidence from public property-casualty insurers

We examine the heterogeneous impacts of asset, product, and financial risks on property-casualty insurers' performance and their resilience to the COVID-19 pandemic. To address the potential endogeneity problem in risk taking, a panel dataset of public property-casualty insurers around the world is studied using a two-stage least squares analysis. The results show that asset and product risks enhance performance, while financial risk reduces it. In addition, property-casualty insurers are resilient to the adverse effects of COVID-19, with the exception of insurers with high asset and product risks. These findings highlight the importance of considering different dimensions of risk when evaluating property-casualty insurers. Regulators, investors, and analysts should take into account the potential impact of asset, product, and financial risks on insurers' performance and resilience, particularly in the context of the COVID-19 pandemic.

Do Co-opted Boards Affect the Financial Performance of Insurance Firms?

AbstractWe examine the performance-effects of Chief Executive Officer (CEO) co-opted boards in United Kingdom (UK) property-casualty insurers. We report that board insiders appointed in the aftermath of CEO succession reduce profitability, but bolster solvency. Enhanced solvency also results when the CEO is a financial expert and when proportionately more inside directors are selected by a CEO who is a financial expert. We further find enhanced profitability-effects for insurance experienced co-opted outside directors, while large investors improve solvency. However, the internal or external origin of the CEO does not affect financial outcomes. We consider that our results could have commercial and/or public policy implications.

Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting

Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, independence between the claim frequency and severity, and assign full credibility to the data. To overcome some of these restrictions, this paper investigates the predictive performance of Gradient Boosting with decision trees as base learners to model the claim frequency and the claim severity distributions of an auto insurance big dataset and compare it with that obtained using a standard GLM model. The out-of-sample performance measure results show that the predictive performance of the Gradient Boosting Model (GBM) is superior to the standard GLM model in the Poisson claim frequency model. Differently, in the claim severity model, the classical GLM outperformed the Gradient Boosting Model. The findings suggest that gradient boost models can capture the non-linear relation between the response variable and feature variables and their complex interactions and thus are a valuable tool for the insurer in feature engineering and the development of a data-driven approach to risk management and insurance.

Storm CAT Bond: Modeling and Valuation

The frequency and intensity of weather-related catastrophes, such as storms and floods, have been increasing due to climate change. This leads to rising storm catastrophe risks to the property–casualty insurance and reinsurance sector. In this article, we propose an index-based storm catastrophe (CAT) bond for reinsurers to hedge catastrophe risks related to storm losses. Storm loss data have a large portion of zero values and a continuous positive right-skewed distribution, together with high-dimensional spatial dependence. We address these unique properties by proposing a general two-part autoregressive (2PAR) distribution as the marginal model and a semicontinuous spatiotemporal vine copula as the dependence model. We investigate the CAT bond market equilibrium and endogenously solve for the optimal market price of risk and coupon rates. Our empirical results using historical loss data at the county level in Florida show that the proposed CAT bond can stabilize the reinsurer’s cash flows and create attractive returns to investors by offering high coupon rates. Our framework can be generalized to design and price other catastrophe financing facilities.

Analyst coverage, executive compensation and corporate risk‐taking: Evidence from property–casualty insurance firms

AbstractUsing an exogenous drop in analyst coverage introduced by broker closures and mergers, we test for the causal impact of analyst coverage on corporate risk‐taking, in an opaque industry. We document an increase in risk using several book‐based and market‐based risk measures, including tail and default risk measures. Results are driven by firms with stronger managerial risk‐taking compensation incentives. The increase in risk is stronger in more opaque firms, and firms with weaker policyholder monitoring. Firm risk increases through at least one risk‐taking action, such as investing firm assets in higher‐risk bonds. Our study highlights the importance of stock analysts in affecting corporate risk‐taking, especially in the presence of stronger managerial, compensation risk‐taking incentives.

Underwriting Cycles in Property-Casualty Insurance: The Impact of Catastrophic Events

This paper challenges the question of existence and predictability of underwriting cycles in the U.S. property and casualty insurance industry. Using an approach in the frequency domain, we demonstrate the existence of a hidden periodic component in annual aggregated loss ratios. The data support an underwriting cycle length of 8–9 years. Going beyond previous research and studying almost 30 years of quarterly underwriting data, we can improve forecasting performance by (dis)connecting cycles and catastrophic events. Superior out-of-sample forecast results from models with intervention variables flagging the time point of catastrophic outbreaks is achieved in terms of mean squared/absolute forecast errors. We evaluate model confidence sets containing the most accurate model with a certain confidence level. The analysis suggests that reliable forecasts can be achieved net of the irregular major peaks in loss distributions that arise from natural catastrophes as well as unusual “black swan” events.

Regulation of Property-Casualty Insurance by the Executive and Legislative Branches (a Casebook-Style Chapter)

Regulation of Property-Casualty Insurance by the Executive and Legislative Branches (a Casebook-Style Chapter)

Are Female CEOs Associated With Lower Insolvency Risk? Evidence From the U.S. Property-Casualty Insurance Industry

Are Female CEOs Associated With Lower Insolvency Risk? Evidence From the U.S. Property-Casualty Insurance Industry

The Effect of External Monitoring on Conservative Financial Reporting in the Property-Casualty Insurance Industry

SUMMARY When property-casualty (P&C) insurers understate their loss reserves, they increase the appearance of solvency. Understating loss reserves can lead to inadequate loss protection for policyholders. This paper investigates the effect of external monitoring by state regulators on financial reporting by P&C insurers. Specifically, we analyze whether a regulatory financial examination of P&C insurers, as mandated and conducted by state law, results in reporting more conservative loss reserve estimates. We find that P&C insurers report more conservative loss reserve estimates during and following a regulatory financial examination. Whereas past studies use audits as external monitoring events around which fluctuations in loss reserves are observed, we focus on regulatory financial examinations as external monitoring events that mitigate the use of loss reserves to manage perceptions of company health. JEL Classifications: G22; H79; M42; M48.

Accounting Discretion and Loss Reserve Discounting: Evidence from the Canadian Property-Casualty Insurance Industry

Accounting Discretion and Loss Reserve Discounting: Evidence from the Canadian Property-Casualty Insurance Industry

Accruals quality and analyst forecast accuracy: evidence from the property-casualty insurance industry

This paper examines the association between property-casualty insurer accruals quality and analysts' earnings forecasts (i.e., accuracy and dispersion of forecasts). Using insurer-specific accruals, loss reserves, we calculate accruals quality which can be decomposed into its innate and discretionary components. Our results provide evidence that higher accruals quality - as measured by lower standard deviation of loss reserve errors - is positively associated with analysts' forecast accuracy. In other words, our results suggest that analysts provide less accurate forecasts for firms with higher reserve error volatility. Also, we show that lower accruals quality is associated with higher forecast dispersion indicating more disagreement among analysts. Our results hold consistent with decomposed components of accruals, innate and discretionary, and conclude that both managerial discretion and basic operations of firms affect insurers analysts' earnings forecasts.

Accruals quality and analyst forecast accuracy: evidence from the property-casualty insurance industry

Accruals quality and analyst forecast accuracy: evidence from the property-casualty insurance industry

Analyst Coverage and Corporate Risk-Taking: Evidence From Property-Casualty Insurance Firms

We test for the causal impact of analyst coverage on corporate risk-taking in the property and casualty insurance sector, using the exogenous change in analyst coverage introduced by broker closures and mergers. We find that a decrease in analyst coverage promotes an increase in insurers’ risk-taking, which is mainly driven by insurers with smaller initial analyst coverage and those operating in an environment of lower product market competition. We also show that the decrease in analyst coverage causes more risky investment behaviors, more risky underwriting, and less conservative reserving practice.

Are all mutuals the same? Evidence from CEO turnover in the US property–casualty insurance industry

Organizational form in insurance has been widely studied in the prior literature. Although researchers have recognized sub-types of stock insurers, mutuals have always been considered as a single homogeneous category. To the extent that mutual sub-types behave differently, considering all mutuals the same can result in misleading conclusions. The present study aims to remedy this gap in the literature by considering the full range of mutual firm types. Mutuals are classified as family-controlled, association-controlled, and pure mutuals with various sub-types within each group. We analyze corporate governance among mutual sub-types in the U.S. property–casualty (P–C) insurance industry by studying CEO turnover, particularly nonroutine turnover. Multinomial probit analysis is used to test for relationships between mutual sub-types and turnover. The principal finding is that all mutuals are not the same. The probability of nonroutine CEO turnover is lowest for family-controlled, non-association mutuals and highest for association-controlled mutuals. Non-association mutuals with family-member CEOs have the lowest turnover rates among all ownership types. Thus, future research should utilize more detailed organizational form categories than the traditional literature.