Volume 4, Issue 2

How do property liability insurance companies choose their organizational forms and distribution channels? Prior studies have not yet provided a consistent conclusion. In this paper, we propose a reduced form approach to reexamine the relationship between organizational forms and distribution channels in the insurance industry, using cross-sectional data pertaining to U.S. property liability insurance companies in 2004. We adopt a conditional dependence test, which can overcome the sensitivity problem of the structural form setting. The results show that after we control for all explanatory variables, the relationship between organizational forms and distribution channels is conditionally uncorrelated. The result is consistent with Regan and Tzeng (1999), but contradicts the findings of Baranoff and Sager (2003) and Kim et al. (1996).

This article analyzes the life and nonlife insurance markets with horizontal differentiation and brand loyalties using a spatial competition model. We construct a three-stage game that includes sales promotion in the first stage, insurance product characteristics in the second stage, and premiums in the third stage. The analysis derives the following results. First, an insurance firm with greater brand loyalty realizes a higher degree of differentiation and sets a relatively higher premium. Second, introducing regulation in insurance product characteristics leads to a lower degree of differentiation and a lower level of sales promotion. Such regulation may then be desirable in terms of social surplus.

Empirical measurement of dependency between different lines of non-life insurance business remains an open problem. International regulatory requirements and accounting standards are moving towards increased disclosure of the variability of insurance liabilities. In estimating the variability, it is required to take diversification benefits into account, as it is generally believed that different lines of business are not perfectly dependent on one another. Due to data limitations and the complicated nature of non-life insurance business, however, direct quantification of dependency and so diversification benefits between individual lines is a difficult task. In this article, we set forth some practical considerations in modeling dependency and diversification benefits under Australian regulatory environment. We start with providing a summary of the underlying factors causing dependency and examining the reasonableness of some industry correlation figures based on these factors. We also study the correlations between the historical loss ratios of several lines of Australian business. Afterward we carry out extensive simulation studies to investigate the effects of applying some recently suggested methodologies for tackling the problem of evaluating diversification benefits.

This paper investigates the asymptotic behavior of tail probability of a randomly weighted sum of real-valued heavy-tailed dependent random variables; the weights form another sequence random variable. Under some other mild conditions, the asymptotic relations obtained are further applied to derive asymptotic estimate for ruin probabilities in a discrete time risk model.

The survival mixture model, which is an extension of the ordinary survival model that allows the existence of a fraction of the borrowers to be risk-free, is applied to credit risk analysis. In a regression setting, the effect of borrowers' characteristics on both the risk-free probability and default risk can be assessed simultaneously. Using the C statistic as a measure of accuracy, the survival mixture model shows improved power to discriminate between ‘good' and ‘bad' customers, when compared with other commonly used statistical models for credit risk analysis. A simulation study is conducted to assess the performance of the proposed numerical estimation method. The survival mixture model not only concentrates on the time-to-default of the borrowers, it also predicts the probability of being risk-free. It provides additional information about the borrowers' default risk in relation to their characteristics, which assists the lending institutions to better manage credit risk.

The underlying rationale for insurance purchases and other forms of risk management is aversion to risk, yet the measurement of risk aversion has been inappropriately focused on risks of little or no consequence. The present paper demonstrates that a quantitative measure of large-scale risk aversion can be constructed with elementary mathematics, facilitating the solution of numerical problems. We illustrate its use with hypothetical examples and empirical survey data.