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We study and calibrate a cohort-based model which captures the characteristics of a mortality surface with a parsimonious, continuous-time factor approach. The model allows for imperfect correlation of mortality intensity across generations. It is implemented on UK data for the period 1900-2008. Calibration by means of stochastic search and the Differential Evolution optimization algorithm proves to yield robust and stable parameters. We provide in-sample and out-of-sample, deterministic as well as stochastic forecasts. Calibration confirms that correlation across generations is smaller than one.

We formulate, study and calibrate a continuous-time model for the joint evolution of the mortality surface of multiple populations. We model the mortality intensity by age and population as a mixture of stochastic latent factors, that can be either population-specific or common to all populations. These factors are described by affine time-(in)homogenous stochastic processes. Traditional, deterministic mortality laws can be extended to multi-population stochastic counterparts within our framework. We detail the calibration procedure when factors are Gaussian, using centralized data-fusion Kalman filter. We provide an application based on the mortality of UK males and females. Although parsimonious, the specification we calibrate provides a good fit of the observed mortality surface (ages 0-99) of both sexes between 1960 and 2013.

This work provides an investigation of the presence of spatial variability in the determinants of mortality rates. Specifically, by using the age‐adjusted mortality rates of the counties of the contiguous United States, this research applies a multiscale geographically weighted regression (MGWR) approach to examine the spatial variations in the relationships between mortality rates and a diverse group of associated determinants. The results demonstrate that the MGWR approach produces a differentiable account of the global, regional, and local effects acting on mortality rates across the United States. Thus, this work lays the groundwork for the consideration of spatial varying effects on mortality rates, which operate at different spatial scales.

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In this paper, we present and calibrate a multi-population stochastic mortality model based on latent square-root affine factors of the Cox-Ingersoll and Ross type. The model considers a generalization of the traditional actuarial mortality laws to a stochastic, multi-population and time-varying setting. We calibrate the model to fit the mortality dynamics of UK males and females over the last 50 years. We estimate the optimal states and model parameters using quasi-maximum likelihood techniques.

The aim of this paper is to state a correspondence between marked Poisson processes and multivariate subordinated Lévy processes. We prove that, under suitable conditions, marked Poisson processes are in law subordinated Brownian motions and we provide their Lévy triplet and characteristic function. We introduce the class of multivariate Gaussian marked Poisson processes and prove that -in law -they are multivariate subordinated Brownian motions.