The implications of genetic testing information availability for society, medicine, employment, and individual privacy rights have generated much political debate, legislation and academic research. Part of this debate centres on the ethical and economic considerations resultant from this expanded knowledge, particularly for insurance practices. Within insurance economics, the possibility of adverse selection has been debated and the potential for a ban on an insurer's use of genetic testing has been studied with respect to whether or not such a ban might actually result in insurance market failure due to this adverse selection. Studies have examined the issue using expected loss cost (actuarial or ‘fair’) pricing models, and have not considered either equilibrium (supply and demand) price setting as is present in markets, or the potentially swamping effect of background health care risks facing the insured, having nothing to do with any particular genetic mutation. Here we construct a supply and demand function with both high and low risk individuals in the presence of background health care cost risks, and derive an equilibrium price and market composition to determine whether, if genetic information is allowed for individuals, but this same information is not shared with insurers: (1) is market failure inevitable? (it is not if the background risk is sufficiently high relative to potential genetic risk costs); (2) will equilibrium prices result in all low risk insured exiting the market? (not in the presence of significant background risk); and (3) how much would prices increase and market sales decrease if insurers do not have the same genetic information as the insured? (prices will increase, but not necessarily very much in the presence of background risk, and not as much as that previously estimated in the insurance literature).

This article discusses risk classification and develops and discusses a framework for estimating the effects of restrictions on risk classification. It is shown that expected losses due to adverse selection depend only on means, variances and covariances of insurance factors and rates of uptake of insurance. Percentage loadings required to avoid losses are displayed. Correlated information, such as family history, is also incorporated and it is seen how such information limits losses and decreases required loadings. Although the evidence suggests that adverse selection is not, at present, a severe problem for insurers, this might change if the authorities impose restrictions on risk classification and/or customers gain an informational advantage (such as better knowledge of their own risk levels). Application is made to unisex annuity pricing in the UK insurance market.

Standard optimal portfolio choice models assume that investors maximise the expected utility of their future outcomes. However, behaviour which is inconsistent with the expected utility theory has often been observed.

In a discrete time setting, we provide a formal treatment of risk measures based on distortion functions that are consistent with Yaari’s dual (non-expected utility) theory of choice (1987), and set out a general layout for portfolio optimisation in this non-expected utility framework using the risk neutral computational approach.

As an application, we consider two particular risk measures. The first one is based on the PH-transform and treats the upside and downside of the risk differently. The second one, introduced by Wang (2000) uses a probability distortion operator based on the cumulative normal distribution function. Both risk measures rank-order prospects and apply a distortion function to the entire vector of probabilities.

This paper proposes a multivariate extension of the equilibrium pricing transforms for pricing general financial and insurance risks. The multivariate Esscher and Wang transforms are derived from Bühlmann’s equilibrium pricing model (1980) under some assumptions on the aggregate risk. It is shown that the Esscher and Wang transforms coincide with each other when the underlying risks are normally distributed.

In this paper we revisit an economic model of Buhlmann (ASTIN Bulletin, 1980) and derive equilibrium pricing transforms. We obtain the Esscher Transform and the Wang Transform under different sets of assumptions on the aggregate economic environment. We show that the Esscher Transform and the Wang Transform exhibit very different behaviors when used in pricing insurance risks.