A Study On The Pricing Of Hybrid Pension Products Under The Stochastic Volatility Model Based On Machine Learning

With the intensifying process of population aging,the issue of old-age care has become an important issue to ensure people’s livelihood and promote social stability and development.The important role of pension products in the social security system for the elderly has become increasingly prominent.Pension plans involve the inter-period actuarial relationship between contributions and benefits.Hybrid pension plans have various advantages such as plan flexibility,benefit security,and risk sharing,and have excellent development prospects.The price of pension products is the initial fee to enter the pension plan.In-depth research on how to accurately and quickly price hybrid pension products is conducive to a deeper understanding of the operating mechanism and actuarial mechanism behind it,and promotes the regulatory authorities to further strictly formulate relevant regulations and improve the level of supervision.At the same time,it can comprehensively balance the plan managers’ operational management efficiency and the plan participants’ degree of protection of rights,which can promote the healthy and sustainable development of the pension insurance market.This paper adopts a combination of theoretical and empirical methods to study the pricing of hybrid pension products under the stochastic volatility model.This paper transforms the pricing of pension products into option pricing,and designs a relevant pricing algorithm under the constant volatility model using the idea of delta hedging.Through comparative demonstration,the pricing algorithm is further extended to the stochastic volatility model.Since the rate of return of the underlying asset plays a pivotal role in the pricing result,an algorithm based on machine learning is also constructed in this paper to more accurately predict the rate of return of the underlying asset.And through empirical research,the validity of the prediction method is guaranteed.Finally,based on the real market data,this paper constructs relevant pension products,and conducts pricing simulation and parameter impact analysis.This paper first selects the monthly return data of Shanghai and Shenzhen A-share listed companies,and uses decision tree algorithm and random forest algorithm to conduct an empirical study on the predictability of China’s stock market returns.While verifying the predictability,this provides an empirical research basis for the empirical simulation of hybrid pension product pricing.Then,using the core idea of delta neutrality,the model selection is further extended from the Black-Scholes model to the Heston stochastic volatility model.Based on this,a hybrid pension product pricing model is constructed.Then,by using the real data of financial market and empirical research data to conduct empirical simulation,the pricing of hybrid pension products is realized.The main research results of this paper show that:(1)the machine learning algorithm can effectively realize the out-of-sample prediction of stock excess returns,and the constructed long-short investment portfolio has a good performance;(2)The pricing method designed in this paper can indeed make the simulated mixed pension product prices more in line with the real market conditions,and promote the rationality of product prices and the healthy development of the pension insurance market;(3)The hybrid pension product pricing model established based on the Heston model achieves more reasonable pricing by incorporating stochastic volatility factors,and multiple characteristics of volatility will have varying degrees of impact on the price of the hybrid pension product.