1,721,060 research outputs found
Tackling Non-Communicable Diseases by a forecasting model for Critical Illness Cover
Full text linkNon-communicable diseases are the most frequent causes of death in most countries in the Americas, the Eastern Mediterranean, Europe, South-East Asia, and the Western Pacific. In the African Region, there are still more deaths from infectious diseases than NCDs. WHO projections show that NCDs will be responsible for a significantly increased total number of deaths in the next decade (WHO, 2014). In this context, the market of illness insurance is strongly being developed, allowing policyholders to reduce the financial impact of diseases. Indeed, critical illness insurance typically provides a payment of a lump sum in the event of the person insured suffering a condition covered under the policy. In other words, the insured receives a fixed sum on the diagnosis of a specified list of critical illnesses. The contract terms may also be structured to pay out regular income cash-flows on the policyholder. In general, since the policy face amount has to be paid on diagnosis, the incidence rates or diagnosis rates have to be accurately estimated. The research is here developed around the following focal and original points: • the estimation of the diagnosis rates by means of an analysis by cause of death for obtaining cause-specific diagnosis rates: in particular, the author modelі the probability of death by cause as a proxy of the estimate of the diagnosis rates; • the cause-specific death rates are modelled by a stratified stochastic model for avoiding the durable problem in literature of the dependence among different causes of death; • a fair valuation framework is adopted for pricing a specific product of critical illness insurance. The analysis is completed by empirical finding
The Future Human Lifespan: A study on Italian Population
No full textIn the latter part of the 20th century, continued improvements in living standards, health behaviors,
and medical care reduced mortality and produced amazing advances in life expectancy.
These trends, followed by all industrial nations, decidedly affect the financial position of an insurance
company, interested in the construction of updated life tables. The approach to this problem
is faced in this paper by using the Lee-Carter methodology. In particular, in the present work,
we are interested in modeling and forecasting mortality and life expectancy on a period basis
through the use of a stochastic forecasting method which uses time-series models to make
long-term forecasts
Lee-Carter mortality forecasting: application to the Italian population
No full textIn this paper we investigate the feasibility of using the Lee-Carter methodology to construct mortality forecasts for the Italian population. We fit the model to the matrix of Italian death rates for each gender from 1950 to 2000. A time-varying index of mortality is forecasted in an ARIMA framework and is used to generate projected life tables. In particular we focus on life expectancies at birth and, for the purpose of comparison, we
introduce an alternative approach for forecasting life expectancies on a period basis.
The resulting forecasts generated by the two methods are then compared
Measuring and hedging the basis risk by Functional Demographic Models
No full textLongevity phenomenon is a relevant aspect for insurance
companies which are obliged to quantify the impact of uncertainty of
mortality trend on issued products, in order to manage the risk derived
from it. Recently, significant tools have been developed for transferring
longevity risk to the capital markets, bringing additional capacity, flex-
ibility and transparency to complement existing insurance solutions. In
particular, hedging longevity risk with index-based longevity hedges can
have several advantages. Nevertheless, the difference between the in-
surer’s mortality experience based on annuitant mortality and the hedged
standardized index based on reference population mortality give rise to
the so-called basis risk. The presence of basis risk means that hedge ef-
fectiveness will not be perfect and that, post implementation, the hedged
position will still have some residual risk. The present paper seeks to con-
tribute to that literature by setting out a framework for quantifying the
basis risk. In particular we propose a model that measure the population
basis risk involved in a longevity hedge, in the functional demographic
model setting. Moreover, while most existing models are designed for a single population the research objective is to model mortality of two pop-
ulations, in order to align with the hedging purpose. Finally, longevity
hedging strategies are developed by involving mortality-linked securities
- …
