IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH)
Year:2014 | Volume:28 | Issue:4 (112)|Papers Count:9

Mortality Predictions can be made in two ways: First, indirect prediction through life expectancy and then conversion to age specific mortality rates, and the second is direct prediction of mortality rates. In the first approach, it is assumed that after the transition period, which is generally a higher level of life expectancy at birth of 70 years, life expectancy increments decrease and that the increase will be negligible asymptotically. In the second approach, age specific mortality rate is predicted to obtain life expectancy using the direct method of life table construction. Logically, the correct and most plausible method of estimation and to predict life expectancy is the second approach. In addition, usually the indirect method has larger odds and errors. The purpose of this paper is to use the second approach and predict mortality rates using Iranian mortality data in the Lee - Carter model for life insurance calculations. This model has two main factors: period and age. An important and difficult assumption of this model is the relative stability of age pattern of mortality. As various evaluations have shown, under these conditions error of the Lee - Carter model is less than any other method in direct predicting of mortality rates. For Iranian data, our assessments show that the estimation error is small, although this element is higher in some ages. However, hypothesis tests on the stability of the age pattern of mortality in Iran, is the subject of future research.

Competition in insurance industry, due to the expansion of insurance demand in all its fields, especially in life insurance, has highlighted the significance of information distribution. Therefore, it is important to consider issues such as adverse selection, in order to prevent low pricing and insurer's bankruptcy risks. We have used life insurance data of an insurance company to test this phenomenon. According to the Rothschild-Stiglitz theory of separating equilibrium, high risk policyholders demand full coverage with fair premiums while low risk policyholders demand partial coverage with lower (and fair) premiums. As a result of this, using EVeiws software and the reduction in life insurance full coverage (decrease in death benefit) we have identified that different variables affect the level of policyholder's risk (death risk) which is intuitive.Finally, we calculate a risk index using statistically significant variables (which show increasing and decreasing risk levels for policyholders with positive and negative signs). The significance of annual premium and life insurance demand as an index of adverse selection is also estimated. Thus the hypothesis of adverse selection is not rejected.

This paper proposes a new probabilistic model for portfolio optimization based on insurer's liabilities. This model satisfactorily takes into account the stochastic nature of insurer's paid and received amounts. We perform simulations and compare results with the well known Markowitz (1952) model. Global cost of gold and iron ore were used in order to show the model's efficiency in the real world. The results indicate an improved performance of the proposed model in comparison with the Markowitz model. In conclusion, we find the risk and return of assets for this model is more desirable for investments.

In today's business world, management has recognized that customers are the core of business activities. The success and effectiveness of organizations are dependent upon the image of an organization and the way its products are presented to customers. The importance of this point is compounded when in strategic industries, such as insurance industry, which experiences intensified competition with increasing number of market participants, selecting appropriate brand strategies will bring more benefits. The purpose of the present paper is to identify success factors in brand management, in association with dual responses of customers. This paper can be seen as an applied paper based on its objects and a descriptive-survey paper based on its data collection method. The results of this paper are obtained through structural equations model and indicate that relative priority of constituent elements of brand equity is with brand loyalty whereas, price is the most important determining factor in the choice of the insured.

In this paper we consider deregulation as an effective ingredient for insurance companies' organizational structure when organizational structure is intended as a dependent variable. Our fmdings here is based on the views of 168 executives and assistants in central branches of private insurance companies in Tehran, with at least 5 years experience in insurance industry. These data were analyzed using covariance structure (structural equations modeling) and confirmatory factor analysis with parametric tests to compare group correlations. The results indicated that after deregulation, insurance companies' organizational structure will change and formalization, centralization becomes more focused. In fact, after deregulation, msurance companies' organizational structure will be more automated.

In this paper we analyze asset risks of an insurance company in two different categories including investment in stocks and real estate. To calculate stock investment risk factors, we use daily stock index of Tehran Stock Exchange's main index (TEPIX, 2006-2010), and Index of monthly Private Housing Rental Prices of Iranian urban areas (2002-2011). We analyze different characteristics of index distribution and calculate their corresponding value at risk (VaR). In order to test the validity of proposed estimations, we conduct a back- test using data for the last three-years (2010-2012). The results show that EGARCH model has a higher precision and performance in contrast to other models. Moreover, it is clear that the calendar effect exists in Iran stock market. By adjusting our data for calendar effect, we show that the stock market risk factor decreased by 23%.

A life insurance contract is considered as a free contract which follows common rules and doesn't include judicial and legal barriers. Sharia jurists have admitted that a life insurance contract is a legitimate and free contract due to considerations of common authenticity principles, without confining contracts in special and conditional frameworks because of lacking judicial barriers like unlawful profit, gambling and abeyance. However, they believe that life insurance contracts can be concluded as in special contracts like peace, gifts made in consideration and limited partnerships, due to judicial objections raised in life insurance contracts. This article compares life insurance contract with similar contracts to analyze its judicial and legal nature and also the independence of this contract is investigated as a right and legal contract.

The purpose of this paper is to design a conceptual model for the assessment of entrepreneurial opportunities in life insurance. We apply a mixed method approach with semi-structured interviews with life insurance experts (both academic and administrative experts. Interviews were analyzed by open, axial and selective coding methods, and opportunity assessment criteria were driven from interviews. In the axial coding stage, 36 criteria were driven and classified into 8 general factors including "human resources, available and required administrative capabilities", "product", "target market and demographic groups", "laws, regulations, and administrative infrastructures", "threat of competitors", "profitability of the opportunity", "barriers to entry" and "uncertainty and ambiguity in market". The criteria were drafted into a questionnaire to assess the importance of each criterion. The questionnaires were distributed among life insurance managers of selected insurance companies' main branches in Tehran and 87 questionnaires were collected. The validity of the questionnaire was tested through Cronbach's alpha test (alpha=0.93). SPSS software and factor analysis method were used to analyze the data. After factor analysis, some factors were omitted and another factor named "entrepreneurial strategy" was added to the above-mentioned factors. Finally the assessment model of entrepreneurial opportunities in life insurance was designed based on nine factors. Based on the results, the profitability of opportunity, product specifications, and the entrepreneur's human resources were the most important factors in assessing life insurance entrepreneurial opportunities.