Are life insurance futures a safe haven during COVID-19?

Wang, Kuan-Min; Lee, Yuan-Ming

doi:10.1186/s40854-022-00411-z

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…It is a true that unpredictable things and good and bad times happen randomly; thus, it is really appropriate to extend the meaning of this sentence to insurance. The so-called "insurance" means that once a risk occurs, it will inevitably cause large or small personal financial losses, but it can be compensated through gathering the resource power (such as money) of everyone from insuring insurance [37]. If you have bought insurance, you can avoid risks and benefit from an agreement of insurance.…”

Section: Ltci and Its Related Application Issues For Developing Poten...mentioning

confidence: 99%

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

Chen

Hung

Lin³

2023

Mathematics

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With the motivation of long-term care 2.0 plans, forecasting models to identify potential customers of long-term care insurance (LTCI) are an important and interesting issue. From the limited literature, most past researchers emphasize traditional statistics techniques to address this issue; however, these are lacking in some areas. For example, intelligent hybrid models for LTCI are lacking, performance measurement of components for hybrid models is lacking, and research results for interpretative capacities are lacking, resulting in a black box scenario and difficulty in making decisions, and the gap between identifying potential customers and constructing hybrid models is unbridged. To solve the shortcomings mentioned above, this study proposes some advanced intelligent single and hybrid models; the study object is LTCI customers. The proposed hybrid models were used on the experimental dataset collected from real insurance data and possess the following advantages: (1) The feature selection technique was used to simplify variables for the purpose of improving model performance. (2) The performance of hybrid models was evaluated against some machine learning methods, including rough set theory, decision trees, multilayer perceptron, support vector machine, genetic algorithm, random forest, logistic regression, and naive Bayes, and sensitivity analysis was performed in terms of accuracy, coverage, rules number, and standard deviation. (3) We used the C4.5 algorithm of decision trees and the LEM2 algorithm of rough sets to extract and provide valuably comprehensible decisional rules as decision-making references for the interested parties for their varied benefits. (4) We used post hoc testing to verify the significant difference in groups. Conclusively, this study effectively identifies potential customers for their key attributes and creates a decision rule set of knowledge for use as a reference when solving practical problems by forming a structured solution. This study is a new trial in the LTCI application field and realizes novel creative application values. Such a hybrid model is rarely seen in identifying LTCI potential customers; thus, the study has sufficient application contribution and managerial benefits to attract much concern from the interested parties.

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Section: Ltci and Its Related Application Issues For Developing Poten...mentioning

confidence: 99%

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

Chen

Hung

Lin³

2023

Mathematics

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“…[ 53 ] have also found that green bonds could be a safe haven, but noted that they were not as effective as hedging financial sectors. [ 54 ] have examined insurance companies in India and Taiwan to see if they were safe havens during COVID-19. They have found that insurance futures served as a safe haven for the markets in these two countries, which suffered from high rates of infection.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the hedge performance of US dollar, VIX, and gold during the coronavirus pandemic: Is US dollar a better hedge asset?

Yun,

Choi,

Kim

2023

PLoS ONE

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This study utilizes the hedging potential of the U.S. Dollar Index (USDX) during the COVID-19 period, specifically comparing its positive effects on optimal portfolio weights and hedging ratios with those of traditional hedging assets, such as the VIX and gold. The scalar BEKK GARCH model is employed to forecast volatility and calculate hedging indicators. The results show that USDX exhibits strong hedging abilities against S&P 500 index volatility. These findings highlight the advantageous role of the USDX as a hedging instrument, particularly during periods of heightened market uncertainty, such as during the COVID-19 crisis. Despite the increased market volatility during the COVID-19 pandemic, the value of the optimal portfolio weights is stable and the volatility of the weights is significantly reduced, demonstrating the strength of the USDX’s low risk and volatility in hedging against market fluctuations. Moreover, the increase in the hedge ratio indicates that more capital is allocated to hedging, reflecting the increased correlation between the USDX and S&P 500 index. These results emphasize the beneficial role of the USDX as a hedging instrument during times of elevated market uncertainty, such as during the COVID-19 crisis. Ultimately, USDX can provide valuable insights for market participants seeking effective hedging strategies.

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Determinants for Life Insurances, a View on Notoriety

Câmpeanu

2023

ISF

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Life insurances are among the most important products on a mature market, usually having a consistent share of the market. However, with a long development period behind, the differences between countries are quite relevant and that extends not just in terms of market share and coverage but also on factors influencing the acquisition. Dominated by Motor Third Party Liability and with a life expectancy among the lowest in European Union, the Romanian market has yet to achieve a its potential on life insurance. Life insurance is the most common financial instruments to cover the risk of death, terminal illness, or incapacity to work, also having lifelong saving components or investment (united linked). Among the usual determinants when it comes to life insurance, we find the level of income, education, age, employment rate, financial status, or even economic growth. However, some uncommon factors have their role in this process, like culture, family status or even religion. This study shall address the influence of knowledge and information, specifically the notoriety of life insurance products and its correlation with sales, aiming to boost the sales of life insurances in Romania by analysing the correlation between the use of product and notoriety. The level of education, occupation, income, or area of living also play an important role as there is a direct influence on the use of product or notoriety.

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Are life insurance futures a safe haven during COVID-19?

Cited by 3 publications

References 44 publications

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

Examining the hedge performance of US dollar, VIX, and gold during the coronavirus pandemic: Is US dollar a better hedge asset?

Determinants for Life Insurances, a View on Notoriety

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