Academic journal article Journal of Risk and Insurance

From the Library Shelf

Academic journal article Journal of Risk and Insurance

From the Library Shelf

Article excerpt

PROBABILITY THEORY

Mixtures of Tails in Clustered Automobile Collision Claims, by Guyonne R. J. Kalb (Monash University), Paul Kofman (Monash University), and Ton C. F. Vorst (Erasmus University)

Knowledge of the tail shape of claim distributions provides important actuarial information. The way two techniques commonly used in assessing the most appropriate underlying distribution can be usefully combined is discussed. The maximum likelihood approach is theoretically appealing since it is preferable to many other estimators in the sense of best asymptotic normality. Likelihood-based tests are, however, not always capable to discriminate among nonclasses of distributions. Extremal value theory offers an attractive tool to overcome this problem. It shows that a much larger set of distributions is nested in their tails by the so-called tail parameter.

This paper shows that both estimation strategies can be usefully combined when the data generating process is characterized by strong clustering in time and size. We find that the extreme value theory is a useful starting point in detecting the appropriate distribution class. Once that has been achieved, the likelihood-based EM-algorithm is proposed to capture the clustering phenomena. Clustering is particularly pervasive in actuarial data. An empirical application to a four-year data set of Dutch automobile collision claims is therefore used to illustrate the approach. Insurance: Mathematics and Economics, July 1996, (18)2: 89-107. (Reprinted with permission of North-Holland Publishing Company.)

HEALTH CARE AND INSURANCE

Health Insurance and the Supply of Entrepreneurs, by Douglas Holtz-Eaken (Syracuse University), John R. Penrod (University of Michigan), and Harvey S. Rosen (Princeton University)

Some commentators have suggested that nonportable health insurance impedes people from leaving their jobs to start new firms, so that universal health insurance would significantly enhance entrepreneurial activity. We investigate this belief by comparing wage-earners who become self-employed during a given period of time with their counterparts who do not. By examining the impact of variables relating to the health insurance and health status of these workers and their families, we attempt to infer whether the lack of health insurance portability affects the probability that they become self-employed. The statistical evidence is consistent with a wide range of responses. Journal of Public Economics, October 1996, (62)1-2: 209-235. (Reprinted with permission of North-Holland Publishing Company.)

Alternative Models of Choice Under Uncertainty and Demand for Health Insurance, by M. Susan Marquis (Rand Corporation) and Martin R. Holmer (Hamilton, Rabinovitch, and Alschuler, Inc.)

We test a standard expected utility model and alternative models about how people evaluate risky prospects using data about individuals' preferences among health insurance plans. A model that assumes people evaluate gains and losses asymmetrically and process certain and uncertain outcomes separately provides a better fit than the standard utility model. These findings suggest inertia in health insurance plan choice and that individuals are more responsive to decreases than to increases in the price of insurance. The Review of Economics and Statistics, August 1996, (78)3: 421-427. (Reprinted with permission of The Review of Economics and Statistics.)

LONG-TERM CARE ISSUES

Providing for Long-Term Care: Insurance vs. Trust Saving, by Peter Zweifel (University of Zurich)

In the process of demographic aging, there is a new risk facing the population of industrialized countries, the risk of needing long-term care (LTC.) For example, the number of individuals aged eighty and older (who are believed to be most exposed to this risk) is predicted to grow from 6.3 million (1992) to 17.7 million by the year 2040 in the United States and from 1. …

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