Volume 7, Issue 1

A statistical analysis which provides a risk assessment of nuclear safety based on historical data is conducted. Classical probabilistic models from risk theory are used to analyze data on nuclear power accidents from 1952 to 2011. Findings are that the severities of nuclear power accidents should be modeled with an infinite mean model and, thus, cannot be insured by an unlimited cover.

In this article, we develop a bankruptcy prediction model for Korean firms that utilize logit regression. We find that not only financial accounting ratios but equity market inputs and macro-economic variables are also important predictors of bankruptcy. However, unlike the findings of Campbell et al. (2008), using market value of equity in computing total assets did not improve the model. We compare the model with a Merton-type structural model and find that our model demonstrates a higher prediction power in distinguishing distressed firms from healthy firms. Though our model proves to perform better, we are careful to make a conclusion and rather suggest using several models for the purpose of risk management to reduce model risk.

This paper investigates technical efficiency and productivity growth in Indian life insurance industry in the era of deregulation. The empirical study uses DEA method and Malmquist productivity index to measure and decompose technical efficiency and productivity growth, respectively. The results suggest that the growth in overall productivity is mainly attributed to improvement in efficiency. Higher pure technical efficiency and lower scale efficiency indicate the insurance firms have generally, moved away from the optimal scale over the study period. The truncated regression exploring the main drivers of efficiency in the long run found claims ratio, distribution ratio, and firm-size influence technical efficiency positively. The study also found firms that had both life and non-life businesses are more efficient than firms that has only life insurance business.

These days aging population is a common phenomenon in most countries in the world. As a result, many countries are making great effort to improve the quality of life of elderly people after retirement. A long-term care insurance system is one of the products of those efforts. In the middle of year 2008, long term care insurance as a form of social insurance was introduced in Korea and has been providing various services for people who need assistance with daily living activities. A myriad of opinions from various perspectives such as finance and quality have been suggested for improvement of the system. In order to resolve problems and to reflect suggestions for the purpose of developing the system, the future demand and cost needs to be carefully estimated. Also, various important decisions associated with the insurance policy should be based on the results of the projection. This paper provides a case study of public long-term care insurance in Korea and discusses methods to make projections on the future costs of long-term care insurance.

One of the main characteristics of Income Protection Insurance (IPI) claim duration data, which has not been considered in the actuarial literature on the topic, is left-truncation. Claimants that are observed are those whose sickness durations are longer than the deferred periods specified in the policies, and hence left-truncation exists in these data. This paper investigates a series of conditional mixture models when applying survival analysis to model sickness durations of IPI claimants, and examines the consequence of treating the IPI data with lengthy deferred periods as complete data and therefore ignoring the left-truncation by fitting the corresponding unconditional distributions. It also quantifies the extent of the bias in the resulting parameter estimates when ignoring the left-truncation in the data. Using the UK Continuous Mortality Investigation (CMI) sickness duration data, some well-fitting survival model results are estimated. It is demonstrated that ignoring the left-truncation in certain IPI data can lead to substantially different statistical estimates. We therefore suggest taking left-truncation into account by fitting conditional mixture distributions to IPI data. Furthermore, the best fitting model is extended by introducing a number of covariates into the conditional part to do regression analysis.

This paper uses an Alternating Renewal Process to model the lengths of the health and sickness periods. The first two moments of the discounted aggregate benefits paid out up to an arbitrary time t are then derived.