scholarly journals A Square-Root Factor-Based Multi-Population Extension of the Mortality Laws

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2402
Author(s):  
Petar Jevtić ◽  
Luca Regis
Keyword(s):  
Maximum Likelihood ◽  
Model Parameters ◽  
Time Varying ◽  
Square Root ◽  
Stochastic Mortality ◽  
Mortality Model ◽  
Model Based ◽  
Males And Females ◽  
Quasi Maximum Likelihood ◽  
Stochastic Mortality Model

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.

Pricing longevity-linked derivatives using a stochastic mortality model

2019 ◽  
Vol 48 (24) ◽  
pp. 5923-5942
Author(s):  
Yige Wang ◽  
Nan Zhang ◽  
Zhuo Jin ◽  
Tin Long Ho
Keyword(s):  
Stochastic Mortality ◽  
Mortality Model ◽  
Stochastic Mortality Model

scholarly journals Comparison of Stochastic Mortality Model for Wider Age Range

MATEMATIKA ◽  
2018 ◽  
Vol 34 (2) ◽  
pp. 227-233
Author(s):  
Siti Rohani Mohd Nor ◽  
Fadhilah Yusof ◽  
Arifah Bahar
Keyword(s):  
Risk Exposure ◽  
Longevity Risk ◽  
Model Structure ◽  
Research Directions ◽  
Linear Pattern ◽  
Stochastic Mortality ◽  
Mortality Model ◽  
New Research ◽  
Age Range ◽  
Stochastic Mortality Model

The incorporation of non-linear pattern of early ages has opened new research directions on improving the existing stochastic mortality model structure. Several authors have outlined the importance of encompassing the full age range in dealing with longevity risk exposure by not to ignore the dependence between young and old age. In this study, we consider the two extensions of Cairns, Blake and Dowd model that incorporate the irregularity profile seen at the mortality of lower ages which are Plat and O’Hare and Li. The models’ performances in terms of in-sample fitting and out-sample forecasts were examined and compared. The results indicated that O’Hare and Li model performs better as compared to Plat model

scholarly journals A Bayesian approach to developing a stochastic mortality model for China

2019 ◽  
Vol 182 (4) ◽  
pp. 1523-1560
Author(s):  
Johnny Siu‐Hang Li ◽  
Kenneth Q. Zhou ◽  
Xiaobai Zhu ◽  
Wai‐Sum Chan ◽  
Felix Wai‐Hon Chan
Keyword(s):  
Bayesian Approach ◽  
Stochastic Mortality ◽  
Mortality Model ◽  
Stochastic Mortality Model

scholarly journals RANK-BASED ESTIMATION FOR GARCH PROCESSES

2012 ◽  
Vol 28 (5) ◽  
pp. 1037-1064 ◽  
Author(s):  
Beth Andrews
Keyword(s):  
Maximum Likelihood ◽  
Garch Model ◽  
Maximum Likelihood Estimators ◽  
Dispersion Function ◽  
Model Parameters ◽  
True Parameter ◽  
Finite Samples ◽  
Garch Processes ◽  
Quasi Maximum Likelihood ◽  
The One

We consider a rank-based technique for estimating generalized autoregressive conditionally heteroskedastic (GARCH) model parameters, some of which are scale transformations of conventional GARCH parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given in Jaeckel (1972, Annals of Mathematical Statistics43, 1449–1458). They are useful for GARCH order selection and preliminary estimation. We give a limiting distribution for the rank estimators that holds when the true parameter vector is in the interior of its parameter space and when some GARCH parameters are zero. The limiting theory is used to show that the rank estimators are robust, can have the same asymptotic efficiency as maximum likelihood estimators, and are relatively efficient compared to traditional Gaussian and Laplace quasi-maximum likelihood estimators. The behavior of the estimators for finite samples is studied via simulation, and we use rank estimation to fit a GARCH model to exchange rate log-returns.

Mortality and smoking prevalence: An empirical investigation in ten developed countries

2013 ◽  
Vol 18 (2) ◽  
pp. 452-466 ◽  
Author(s):  
Torsten Kleinow ◽  
Andrew J.G. Cairns
Keyword(s):  
Smoking Prevalence ◽  
Empirical Investigation ◽  
Developed Countries ◽  
Mortality Rates ◽  
Mortality Data ◽  
Death Rates ◽  
Stochastic Mortality ◽  
Mortality Model ◽  
Stochastic Mortality Model ◽  
Country Specific

AbstractWe investigate the link between death rates and smoking prevalence in ten developed countries with the aim of using smoking prevalence data to explain differences in country-specific death rates. A particular problem in building a stochastic mortality model based on smoking prevalence is that there are in general no separate mortality data for smokers and non-smokers available. We show how we can estimate mortality rates for smokers and non-smokers using information about the smoking prevalence in a number of developed countries, and making an additional assumption about the death rates of smokers. We consider this empirical investigation to be the first step towards a consistent mortality model for multiple populations, which will require modelling of country specific differences in mortality, as well as non-smokers’ and smokers’ mortality rates.

scholarly journals Effective Single-Step Posttranscriptional Dynamics Allowing for a Direct Maximum Likelihood Estimation of Transcriptional Activity and the Quantification of Sources of Gene Expression Variability with an Illustration for the Hypoxia and TNFα Regulated Inflammatory Pathway

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
T. D. Frank ◽  
A. J. F. Collins ◽  
A. Cheong
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Maximum Likelihood ◽  
Transcriptional Activity ◽  
Search Algorithms ◽  
Hek293 Cells ◽  
Model Parameters ◽  
Inflammatory Pathway ◽  
Expression Variability ◽  
Model Based

Data analysis methods for estimating promoter activity from gene reporter data frequently involve the reconstruction of the dynamics of unobserved species and numerical search algorithms for determining optimal model parameters. In contrast, we argue that posttranscriptional dynamics effectively behave like a singlestep stochastic process when gene expression variability is relatively low and, half-lives of the unobserved species are relatively small compared to characteristic observation time scales. In this case, by means of maximum likelihood estimators, for which analytical expressions exist, transcriptional activity of gene promoters can be estimated directly from observed gene reporter data without the need for numerical search algorithms and the reconstruction of unobserved variables. In addition, the model-based data analysis approach yields a single variable that measures the effective strength of the sources that give rise to gene expression variability. The approach is applied to conduct a model-based analysis of the inflammatory pathway under hypoxia condition and stimulation with tumor necrosis factor alpha in HEK293 cells.

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