Volume 10, Issue 1 -

Mortality forecasting plays an essential role in designing welfare policies and pricing aged-related financial derivatives. However, most prevailing models do not perform well in mortality forecasting particularly for the elder people. Indeed, the problem of missing category for the elderly is a typical feature in developing countries, because people are shorter-lived in earlier times and hence the mortality is recorded up to fewer age categories. For example, in Taiwan, the mortality is recorded up to an age of 95 before 1997, but as the improvement of life expectancy, the mortality is recorded up to an age of 100 afterwards. This paper proposes several approaches for data imputation to alleviate this systematic missing data problem of the mortality data. Motivated by Lee, and Carter. 1992. “Modelling and Forecasting the Time Series of US Mortality.” Journal of the American Statistical Association 87:659–71 and Renshaw, and Haberman. 2006. “A Cohort-Based Extension to the Lee-Carter Model for Mortality Reduction Factors.” Insurance: Mathematics and Economics 38:556–70, we employ factor models, in which age, period, and cohort are employed as useful effects. Simulation study and an empirical study using mortality data of Taiwan demonstrate the improvement in forecasting using a suitable data augmentation technique.

Long-term equity return models have a substantial impact on calculating reserves and capital requirements for minimum guarantees in variable annuities (VAs). Under a Bayesian statistical framework, we reinvestigate the standard long-term equity return models (independent lognormal, regime-switching lognormal and stochastic log volatility models) with the Korean equity market data. Our empirical analysis shows that the long-term behaviors of the Korean market are best described by the regime-switching lognormal models, and there can be significant difference in the amount of the reserves and the capital requirements depending on the model selection. The long-term nature of the VA contracts requires that more caution be paid for modeling the mean part of the stochastic log volatility model.

Value at risk (VaR) is a widely used measure for financial risks. However, as argued in Taleb (2012), “VaR encourages low volatility, high blowup risk taking which can be gamed by the Wall Street bonus structure.” It was also argued that one reason for this is the limited ability of all quantitative risk measures (including VaR, TVaR and many other modifications) to measure the risk of extreme events (black swans). In this paper, we argue that VaR and its modifications, being short–term in nature, intend to measure extreme risk by creating extreme small probability values. Even if accurate, they might not be effective in communicating risk to people because it is well documented in the psychology literature that humans tend to make irrational decisions when dealing with extreme small probabilities. As such, we propose that long-term risk measures, such as ruin probabilities over a long time horizon, provide a natural approach to avoid small probability values in measuring the risk of extreme events. They could be considered as a vehicle to communicate extreme risks to fund managers, insurance companies, as well as the public, and to help them in making decisions under uncertainty.

In the Asia-Oceania region with its vulnerable infrastructure, people continue to suffer from natural disasters such as earthquakes, cyclones or floods. In order to construct resilient society, the risk control for prevention-reduction of disasters and the risk finance for restoration and revival is indispensable. This article shows the necessity of building reinsurance functions which enables the underwriting of natural disasters in this area and arrangements of its requirements and conditions. The improvement of infrastructure for natural disaster and the alleviating of poverty are the premises of these issues and have close relations with developing financial markets and diversifying financial-insurance measures. This article shines light on the integrated framework of these factors and the methodology. Ensuring resilience against natural disaster must be shared objectives among the countries in this region with low insurance diffusion and multifarious backgrounds of economy, society and culture. The regional partnership and the support of the global community should play an important role for ensuring resilience. Building the reinsurance function of natural disasters in this region follows the same path with the realization of “Principles for Sustainable Insurance”.

In this paper, we model the mortality rate in Thailand by using the Lee-Carter model. Three classical methods, i.e. Singular Value Decomposition (SVD), Weighted Least Square (WLS), and Maximum Likelihood Estimation (MLE) are used to estimate the parameters of the Lee-Carter model. With these methods, we investigate the goodness of fit for the mortality rate spanning the period 2003 to 2012. The fitted models are compared. The autoregressive moving average (ARIMA) is used to forecast the general index and mortality rate the time period from 2013 to 2022. As a result, we also forecast Thai life expectancy at birth.

Publicly provided long-term care (LTC) insurance with means-tested benefits is suspected to crowd out either private LTC insurance (Brown and Finkelstein 2008. The Interaction of Public and Private Insurance: Medicaid and the Long-Term Care Insurance Market. American Economic Review 98(3):1083–102), private saving (Gruber and Yelowitz 1999. Public Health Insurance and Private Saving. Journal of Political Economy 107(6):1249–74; Sloan and Norton 1997. Adverse Selection, Bequests, Crowding Out, and Private Demand for Insurance: Evidence from the Long-term Care Insurance Market. Journal of Risk and Uncertainty 15:201–19), or informal care (Pauly 1990. The Rational Non-purchase of Long-term Care Insurance. Journal of Political Economy 95:153–68; Zweifel and Strüwe 1998. Long-term Care Insurance in a Two-generation Model. Journal of Risk and Insurance 65(1):13–32). This contribution predicts crowding-out effects for both private LTC insurance and informal care on the one hand and private saving and informal care on the other. These effects result from the interaction of a parent who decides about private LTC insurance before retirement and the amount of saving in retirement and a caregiver who decides about effort devoted to informal care. Some of the predictions are tested using a recent survey from China.