基于二维复傅里叶级数展开的最低身故利益保障寿险产品的定价

Valuing Guaranteed Minimum Death Benefi ts by Complex Fourier Series Expansion

  • 摘要: 本文利用二维复傅里叶级数展开(2D-CFS)方法对具有两种对数资产的影响下的最低身故利益保障寿险产品(2D-GMDB)进行定价. 其主要思想是将风险资产(可以是股票基金, 或者是共同基金)的动态价格过程建模为指数Levy过程, 该过程与剩余寿命的密度函数结合构造辅助函数, 并对构造好的满足特定条件的辅助函数进行二维复傅里叶级数展开. 在对级数系数的计算中, 本文主要考虑了两种剩余寿命密度函数的形式, 即联合指数形式和分段常数死亡率形式, 并运用已知对数资产模型的特征指数对级数系数进行计算. 对于对数资产模型的选择, 我们选择了几何布朗运动(GBM) 和跳扩散过程(Jump)模拟对数资产过程. 在数值实验部分, 我们考虑了2D-CFS方法在交换期权、最大期权、最小期权以及几何期权下的定价问题, 其结果与二维余弦级数展开(2D-COS) 方法进行比较, 结果表明无论是在收敛速度上还是计算精度上, 2D-CFS方法都明显优于2D-COS方法.

     

    Abstract: In this paper, a two-dimensional compound Fourier series expansion (2D-CFS) is used to price the guaranteed minimum death benefits (2D-GMDB) under the influence of two logarithmic assets. The main idea is to model the dynamic price process of risk asset (can be equity funds, or mutual funds) as the index Levy process, which is combined with the density function of the remaining life to construct the auxiliary function, and the constructed auxiliary function that satisfies specific conditions is expanded by a two-dimensional complex Fourier series. In the calculation of the series coefficients, this paper mainly considers two forms of remaining lifetime density functions, namely combination-of-exponentials density and piecewise constant forces of mortality assumption, and uses the characteristic index of the known logarithmic asset model to calculate the progression coefficient. For the choice of logarithmic asset model, we selected geometric Brownian motion (GBM) and jump diffusion process (Jump) to simulate the logarithmic asset process. In the numerical experiment section, we consider the pricing problem of the 2D-CFS method under the exchange option, the maximum option, the minimum option and the geometric option, and the results are compared with the two-dimensional cosine series expansion (2D-COS) method, and the results show that the 2D-CFS method is significantly better than the 2D-COS method in terms of convergence speed and calculation accuracy.

     

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