Actuarial Applications and Estimation of Extended CreditRisk+

Hirz, Jonas; Schmock, Uwe; Shevchenko, Pavel V.

doi:10.3390/risks5020023

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…() and Hirz et al . (). In contrast with the other models proposed, Oeppen () used life table deaths to forecast cause‐specific mortality by using compositional data analysis (CODA).…”

Section: Introductionmentioning

confidence: 99%

“…Independent modelling and forecasting of cause-specific mortality is not only unattractive because it ignores dependence patterns among the causes, but also because forecasts often fail to be coherent in the sense that cause-specific deaths must sum to the total number of deaths, which could lead to implausible forecasts. Recently, some mortality forecasting models have been suggested which include dependence between different causes of death and thus incorporate competing risks among causes of deaths: Oeppen (2008), Arnold-Gaille andSherris (2013), Foreman et al (2017) and Hirz et al (2017). In contrast with the other models proposed, Oeppen (2008) used life table deaths to forecast cause-specific mortality by using compositional data analysis (CODA).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Causes of Death by Using Compositional Data Analysis: The Case of Cancer Deaths

Kjærgaard

Ergemen

Kallestrup‐Lamb

et al. 2019

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Cause‐specific mortality forecasting is often based on predicting cause‐specific death rates independently. Only a few methods have been suggested that incorporate dependence between causes. An attractive alternative is to model and forecast cause‐specific death distributions, rather than mortality rates, as dependence between the causes can be incorporated directly. We follow this idea and propose two new models which extend the current research on mortality forecasting using death distributions. We find that adding age, time and cause‐specific weights and decomposing both joint and individual variation between different causes of death increased the forecast accuracy of cancer deaths by using data for French and Dutch populations.

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“…() and Hirz et al . (). In contrast with the other models proposed, Oeppen () used life table deaths to forecast cause‐specific mortality by using compositional data analysis (CODA).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Causes of Death by Using Compositional Data Analysis: The Case of Cancer Deaths

Kjærgaard

Ergemen

Kallestrup‐Lamb

et al. 2019

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…The impact of parameter uncertainty on forecasting mortality rates is documented in Czado et al (2005), Koissi et al (2006) and Kleinow and Richards (2016). A Bayesian approach to mortality modelling via credit risk plus methodology is considered in Shevchenko et al (2015) and Hirz et al (2017aHirz et al ( , 2017b. The rather short time series data used for calibration purpose typically assumed in the literature 1 further enforce the need to account for parameter uncertainty where confidence intervals are required.…”

Section: Introductionmentioning

confidence: 99%

Cohort effects in mortality modelling: a Bayesian state-space approach

Fung

Peters²,

Shevchenko³

2018

Ann. actuar. sci.

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Cohort effects are important factors in determining the evolution of human mortality for certain countries. Extensions of dynamic mortality models with cohort features have been proposed in the literature to account for these factors under the generalised linear modelling framework. In this paper we approach the problem of mortality modelling with cohort factors incorporated through a novel formulation under a state-space methodology. In the process we demonstrate that cohort factors can be formulated naturally under the state-space framework, despite the fact that cohort factors are indexed according to year-of-birth rather than year. Bayesian inference for cohort models in a state-space formulation is then developed based on an efficient Markov chain Monte Carlo sampler, allowing for the quantification of parameter uncertainty in cohort models and resulting mortality forecasts that are used for life expectancy and life table constructions. The effectiveness of our approach is examined through comprehensive empirical studies involving male and female populations from various countries. Our results show that cohort patterns are present for certain countries that we studied and the inclusion of cohort factors are crucial in capturing these phenomena, thus highlighting the benefits of introducing cohort models in the state-space framework. Forecasting of cohort models is also discussed in light of the projection of cohort factors.

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“…The model relies on functional principal component analysis for dimension reduction and a vector error correction model to jointly forecast mortality rates in multiple populations. The usefulness of this model is demonstrated through a series of simulation studies and applications to the age-and sex-specific mortality rates in Switzerland and the Czech Republic.The paper by Jonas Hirz, Uwe Schmock, and Pavel Shevchenko (Hirz et al 2017) introduces an additive stochastic mortality model which allows joint modelling and forecasting of underlying death causes. The model takes its roots from the extended version of the credit risk model CreditRisk+ that allows exact risk aggregation via an efficient numerically stable Panjer recursion algorithm and provides numerous applications in credit, life insurance, and annuity portfolios to derive profit and loss distributions.…”

mentioning

confidence: 99%

“…The paper by Jonas Hirz, Uwe Schmock, and Pavel Shevchenko (Hirz et al 2017) introduces an additive stochastic mortality model which allows joint modelling and forecasting of underlying death causes. The model takes its roots from the extended version of the credit risk model CreditRisk+ that allows exact risk aggregation via an efficient numerically stable Panjer recursion algorithm and provides numerous applications in credit, life insurance, and annuity portfolios to derive profit and loss distributions.…”

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confidence: 99%

Special Issue “Ageing Population Risks”

Shevchenko¹

2018

Risks

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An ageing population is a major challenge for every country in the world arising from the declining fertility rate and increasing life expectancy. A longevity risk (the adverse outcome of people living longer than expected) exacerbated by declining equity returns coupled with the record low interest rate environments has significant implications for societies and manifests as a systematic risk for providers of retirement income products. The aim of this special issue is to highlight advances in quantitative modelling of risks related to ageing population problems. We received an enthusiastic response to the call for research papers and are proud of the special issue now being published. This special issue contains seven research papers.One paper by Marcos Escobar, Mikhail Krayzler, Franz Ramsauer, David Saunders, and Rudi Zagst (Escobar et al. 2016) presents the pricing of variable annuities with guaranteed minimum repayments at maturity and in the case of policyholder death using a closed form approximation. All important risk factors (risky investment asset, interest rate, mortality intensity, and policyholder surrender behaviour) are modelled under an affine linear stochastic framework. The presented pricing framework can be easily implemented, which is important for applications in practice.There are four papers studying and developing advanced stochastic mortality models. The paper by Syazreen Shair, Sachi Purcal, and Nick Parr (Shair et al. 2017) evaluates the forecasting accuracy of two recently-developed coherent mortality models (the Poisson common factor and the product-ratio functional models) designed to forecast the mortality of two or more subpopulations simultaneously. The models are applied to age-gender-specific mortality data for Australia and Malaysia and age-gender-ethnicity-specific data for Malaysia, and the results show that coherent models are consistently more accurate than independent models for forecasting sub-populations' mortality.The paper by Yuan Gao and Han Lin Shang (Gao and Shang 2017) develops a model for the forecasting of mortality rates in multiple populations that combines mortality forecasting and functional data analysis. The model relies on functional principal component analysis for dimension reduction and a vector error correction model to jointly forecast mortality rates in multiple populations. The usefulness of this model is demonstrated through a series of simulation studies and applications to the age-and sex-specific mortality rates in Switzerland and the Czech Republic.The paper by Jonas Hirz, Uwe Schmock, and Pavel Shevchenko (Hirz et al. 2017) introduces an additive stochastic mortality model which allows joint modelling and forecasting of underlying death causes. The model takes its roots from the extended version of the credit risk model CreditRisk+ that allows exact risk aggregation via an efficient numerically stable Panjer recursion algorithm and provides numerous applications in credit, life insurance, and annuity portfolios to derive profit and loss dis...

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Actuarial Applications and Estimation of Extended CreditRisk+

Cited by 8 publications

References 31 publications

Forecasting Causes of Death by Using Compositional Data Analysis: The Case of Cancer Deaths

Forecasting Causes of Death by Using Compositional Data Analysis: The Case of Cancer Deaths

Cohort effects in mortality modelling: a Bayesian state-space approach

Special Issue “Ageing Population Risks”

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