Multistate Actuarial Models of Functional Disability

Fong, Joelle H.; Shao, Adam Wenqiang; Sherris, Michael

doi:10.1080/10920277.2014.978025

Cited by 59 publications

(64 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, a qualified benefit recipient is defined as an individual who has suffered from a medically determinable physical or mental impairment that is expected to result in death or last for at least a year and that prevents the person from engaging in a "substantial gainful activity" (Autor and Duggan 2006). The zero possibility of rejoining a state upon leaving is similar to studies by Buchardt (2014) and Olivieri and Ferri (2003), as opposed to studies conducted by Fong et al (2015), who considered a Markov chain with transient states and an absorbing state.…”

Section: Introductionmentioning

confidence: 97%

Hierarchical Markov Model in Life Insurance and Social Benefit Schemes

Jang

Ramli

2018

Risks

View full text Add to dashboard Cite

We explored the effect of the jump-diffusion process on a social benefit scheme consisting of life insurance, unemployment/disability benefits, and retirement benefits. To do so, we used a four-state Markov chain with multiple decrements. Assuming independent state-wise intensities taking the form of a jump-diffusion process and deterministic interest rates, we evaluated the prospective reserves for this scheme in which the individual is employed at inception. We then numerically demonstrated the state of the reserves for the scheme under jump-diffusion and non-jump-diffusion settings. By decomposing the reserve equation into five components, our numerical illustration indicated that an extension of the retirement age has a spillover effect that would increase government expenses for other social insurance programs. We also conducted sensitivity analyses and examined the total-reserves components by changing the relevant parameters of the transition intensities, which are the average jump-size parameter, average jump frequency, and diffusion parameters of the chosen states, with figures provided. Our computation revealed that the total reserve is most sensitive to changes in average jump frequency.

show abstract

Section: Introductionmentioning

confidence: 97%

Hierarchical Markov Model in Life Insurance and Social Benefit Schemes

Jang

Ramli

2018

Risks

View full text Add to dashboard Cite

show abstract

“…Several studies based on U.S. national data have also been performed. One can refer to Robinson (1996), Pritchard (2006) or more recently Fong et al (2015). On the other hand, studies based on portfolio data (Guibert and Planchet, 2014) are very rare.…”

Section: Introductionmentioning

confidence: 99%

Continuous-Time Semi-Markov Inference of Biometric Laws Associated With a Long-Term Care Insurance Portfolio

Biessy¹

2017

ASTIN Bull.

View full text Add to dashboard Cite

Unlike the mortality risk on which actuaries have been working for more than a century, the long-term care (LTC) risk is young and as of today hardly mastered. Semi-Markov processes have been identified as an adequate tool to study this risk. Nevertheless, access to data is limited and the associated literature still scarce. Insurers mainly use discrete time methods directly inspired from the study of mortality in order to build experience tables. Those methods however are not perfectly suited for the study of competing risk situations.The present article provides a theoretical framework to estimate biometric laws associated with a long-term care insurance portfolio. The presented method relies on a continuous-time semi-Markov model with three states: autonomy, disability and death. The process describing the state of disability is defined through its transition intensities. We provide a formula to infer the mortality of autonomous people from the mortality of the whole portfolio, on which we have more reliable knowledge. We then propose a parametric expression for the remaining intensities of the model. In particular, incidence in LTC is described by a logistic formula. Under the assumption that the disabled population is a mixture of two latent populations with respect to the category of pathology that caused LTC, we show that the resulting intensity of mortality in LTC takes a very peculiar form and depends on time spent in the LTC state. Estimation of parameters relies on the maximum likelihood method. Our parametric approach, while inducing model uncertainty, eliminates issues related to segmentation in age categories, smoothing or extrapolation at higher ages and thus proves very convenient for the practitioner. Finally, we provide an application using data from a real long-term care insurance portfolio.

show abstract

“…We should bear in mind that quantifying the value of change in biometric assumptions is no easy task, given that obtaining timely and accurate estimates of mortality, transition and incidence rates is highly complex for technical reasons [84][85][86][87][88]. In many countries, the lack of biometric data could make it impossible to obtain regular representative estimates of functional status transition rates.…”

Section: The Income Statement: Exploring the Reasons For The Change Imentioning

confidence: 99%

Social Insurance Accounting for a Notional Defined Contribution Scheme Combining Retirement and Long-Term Care Benefits

2018

View full text Add to dashboard Cite

This paper develops a social insurance accounting model for a notional defined contribution (NDC) scheme combining retirement and long-term care (LTC) contingencies. The procedure relies on standard double-entry bookkeeping and enables us to compile a "Swedish" type actuarial balance sheet (ABS) following a framework equivalent to an open group approach. This methodology is suitable for reporting the system's solvency status and can show periodical changes in the system's financial position by means of an income statement. The information underpinning the actuarial valuation is based on events and transactions that are verifiable at the valuation date, without considering expected future trends. The paper also contains an illustrative example to make it easier for policymakers to understand the main advantages and difficulties of our proposal. The policy conclusions stress the need to properly report social insurance benefits to enhance transparency and sustainability and to improve decision-making because it is in the public interest to do so.

show abstract

Multistate Actuarial Models of Functional Disability

Cited by 59 publications

References 28 publications

Hierarchical Markov Model in Life Insurance and Social Benefit Schemes

Hierarchical Markov Model in Life Insurance and Social Benefit Schemes

Continuous-Time Semi-Markov Inference of Biometric Laws Associated With a Long-Term Care Insurance Portfolio

Social Insurance Accounting for a Notional Defined Contribution Scheme Combining Retirement and Long-Term Care Benefits

Contact Info

Product

Resources

About