Gaussian and Affine Approximation of Stochastic Diffusion Models for Interest and Mortality Rates

Abstract

In the actuarial literature, it has become common practice to model future capital returns and mortality rates stochastically in order to capture market risk and forecasting risk. Although interest rates often should and mortality rates always have to be non-negative, many authors use stochastic diffusion models with an affine drift term and additive noise. As a result, the diffusion process is Gaussian and, thus, analytically tractable, but negative values occur with positive probability. The argument is that the class of Gaussian diffusions would be a good approximation of the real future development. We challenge that reasoning and study the asymptotics of diffusion processes with affine drift and a general noise term with corresponding diffusion processes with an affine drift term and an affine noise term or additive noise. Our study helps to quantify the error that is made by approximating diffusive interest and mortality rate models with Gaussian diffusions and affine diffusions. In particular, we discuss forward interest and forward mortality rates and the error that approximations cause on the valuation of life insurance claims

Application of Continuous Non-Gaussian Mortality Models with Markov Switchings to Forecast Mortality Rates

“…Other methods are based, e.g., on autoregressive models (ARiMA [2]), which also include the commonly used the Lee-Carter model (abbreviated as the LC model, [3][4][5][6][7]). Recently, mortality models described by stochastic differential equations have also been developed ( [8][9][10]), including COVID-19 models ( [11][12][13]).…”