2018, Vol.29, No.1

Journal of Insurance and Finance 2023 KCI Impact Factor : 0.59

pISSN : 2384-3209
https://journal.kci.go.kr/kiri

This study empirically analyzed the effects of psychological factors, media exposure, and financial literacy on purchasing intention of accident insurance covering traffic accidents using structural equation model. The study showed that trust in insurers is found to be the most influential factor in the purchase intention. In the case of non-subscribers of accident coverage, media exposure had a positive effect on the new purchase intention of accident coverage as partial mediation of risk sentiment(fear and worry) on traffic accidents. The optimism bias and accident probability positively influenced the new purchase intention of accident coverage as full mediation of risk sentiment for traffic accidents. In addition, trust in government has a direct impact on the purchase intention of new accident coverage in a negative way. On the other hand, for the people who already joined accident coverage, trust in insurers and financial literacy influenced the additional purchase intention of accident coverage in positive and negative way respectively. The findings suggest that insurers, insurance industry, and financial authorities need to continue their efforts to improve the trust of insurance consumers for the sales of accident coverage. Furthermore, it is implied that there is a possibility of adverse selection in accident insurance market as the higher the perceived probability of traffic accident, the higher the purchase intention of accident coverage.

IFRS 17 requires that insurance companies should measure insurance liabilities at each reporting date, using current assumptions such as the amount, timing and uncertainty of future cash flows and discount rates and it is a principle to reflect policyholder behaviors that affect future cash flow. This research analyzes how GMSB costs of interest sensitive whole life insurance change when withdrawal is taken into account. When GMSB cost (PB_GMSB1) is defined as the present value of GMSB claims over the present value of a written premium, the basic analysis shows how PB_GMSB1 decreases when withdrawal is considered. Moreover, the research uses withdrawal models considering policyholder behaviors based on ‟the moneyness of the guarantees (GMSB over surrender value under crediting rate)ˮ or ‟the difference between crediting rate with a minimum guaranteed interest rate and the pricing interest rateˮ. It is shown that PB_GMSB1 applying policyholder behaviors on withdrawal rate is no less than PB_GMSB1 applying the constant withdrawal rate. As a result, it is necessary to apply the dynamic withdrawal rate in GMSB costs evaluation.

We examine the effect of idiosyncratic volatility on expected returns using daily data for common stocks listed on Korean Stock Exchange for the period of January 2000 to December 2015. In particular, we estimate idiosyncratic volatility based on the Carhart(1997) four-factor model in order to control for momentum, a systematic risk for the post-2000 period. Methodology and main findings are as follows. First, although the value-weighted average return differential between the lowest and highest idiosyncratic volatility portfolios is approximately –1.15% per month, the risk-adjusted return is approximately –0.71% per month yet statistically insignificant. Second, we conduct a double-sort portfolio analysis to control for potential effects of firm characteristics. After controlling for turnover, the trading strategy yields –0.86% per month on average, but risk-adjusted return decreases to –0.43% insignificant. Finally, we run Fama and MacBeth(1973) regressions to control for various firm characteristics at the portfolio level. While Idiosyncratic volatility account for the cross-section of returns on idiosyncratic volatility sorts, it becomes insignificant when controlling for turnover. Our findings suggest that there is no robust evidence of a negative relation between idiosyncratic volatility relative to the Carhart(1997) four-factor model and expected returns and that the relationship highly relies on liquidity.