The Research On Longevity Risk Bond Pricing Based On Machine Learning Mortality Model

From the medical point of view,the development of medical technology will lead to the reduction of mortality and the increase of life expectancy in the long run.From the economic point of view,the average life expectancy is positively correlated with the average income level.The actual mortality data also provide preliminary evidence of this trend.However,the rise in life expectancy has also brought longevity risks to life insurers,pension providers,and governments.Moreover,as a systemic risk,longevity risk cannot be dispersed by the law of large numbers.Therefore,how to effectively avoid longevity risk has become a common problem for all sectors.In recent years,as one of the means to avoid longevity risk,longevity bond has attracted attention from all walks of life.There have been many cases of longevity bonds issued abroad.As important data for the pricing of longevity bonds,the measurement and prediction of mortality rate are of great importance to national policies,pension funds,and insurance companies.Simultaneously,as a hot research topic in recent years,machine learning has played its role in many research fields.But since demographers tend to be interested in analyzing specific assumptions,and machine learning algorithms are data-driven,the decisions of these algorithms often cannot be adequately explained,leading to little application of machine learning in the field of mortality prediction.This paper argues that machine learning algorithms can be used as a supplement to,rather than a replacement for,traditional stochastic mortality models.We combined the framework of the LC model,RH model,and APC model with machine learning to predict the mortality rate by sex in China.The results show that compared with the traditional stochastic mortality model,the mortality model combined with the machine learning algorithm generally improves the mortality prediction result,and the mortality model combined with the decision tree algorithm has the best prediction effect on future mortality.Finally,we based on machine learning models to predict China's future mortality,incomplete pricing by wang conversion method to the design of longevity bonds pricing research,and the current situation of population aging of China's current policy Suggestions are given:one is to improve the predictive accuracy of the mortality of standardized mortality rate index compiled;Longevity risk of financial derivatives pricing process will involve the mortality rate index,so the need for pension and endowment insurance of our country security groups,the characteristics of the complementary with electronic information technology,the population mortality data constantly improve and update at the same time,establish an effective and comprehensive database,increase the transparency of the data.Second,improve the pricing mechanism of financial derivatives of longevity risk and increase the rationality of its pricing.Probability distortion methods,such as Wang's transformation,can be incorporated into the pricing process of financial derivatives of longevity risk,so that their prices can be closer to the real risk level of the market.At the same time,a public platform can be considered to normalize the disclosur e of relevant information of the whole industry.