Guy RASOANAIVO - Academia.edu (original) (raw)
Papers by Guy RASOANAIVO
“Multivariate Analysis of Pension Plan Mortality Data”, Charles Vinsonhaler; Nalini Ravishanker; Jeyaraj Vadiveloo; Guy Rasoanaivo, April 2001
North American Actuarial Journal, 2001
This paper uses the logistic regression model to examine private pension plan data for 1989 -95 c... more This paper uses the logistic regression model to examine private pension plan data for 1989 -95 collected by the Retirement Plans Experience Committee of the Society of Actuaries. When only one explanatory variable, such as annuity class size, is used in modeling mortality rates, the model provides a reasonable fit to the data. Multiple explanatory variables give less satisfactory results.
Stochastic modeling of long-term care insurance
One of the key developments in modern actuarial science has been the introduction of stochastic m... more One of the key developments in modern actuarial science has been the introduction of stochastic models. This is necessitated by the design complexity of products being offered in today\u27s marketplace and the many variables that can impact the financial performance of these products. Most of the stochastic modeling has focused on the variables impacting the asset side of an insurance operation like market returns, asset defaults and interest rates. In this thesis, a stochastic model is developed for the Long-Term Care (LTC) Insurance product, focusing on the liability risks like mortality, morbidity and lapse behavior. The model tries to measure the pricing volatility of the LTC rider insurance as well as the stand-alone product, caused by the volatility in these liability risks. The stochastic process governing the model is a Markov Chain process. We first discuss the traditional deterministic pricing of Long-Term Care Insurance. An algorithm is proposed to simulate the LTC insurance process. In addition, a regression model is developed to estimate the volatility risk and thus derive a deterministic approximation to the risk-adjusted net single and level premiums. The robustness of the regression model is tested against the full simulation model, under different assumptions and changes in product design.
Long term care (LTC) pricing requires the use of underlying experience data of mortality and laps... more Long term care (LTC) pricing requires the use of underlying experience data of mortality and lapse rates of healthy insureds, LTC incidence rates, LTC utilization rates, and LTC termination rates from recovery or death. It is important that the pricing process uses the most up-to-date industry tables, modified for a company's own experience and anticipated changes due to improvements in medical science and technology. While companies have recognized this fact, less attention has been paid on the mortality of healthy insureds. It is customary for companies to use either industry experience tables or their own experience of mortality on life insurance in LTC pricing. Therein lies the subtle error that results: Life insurance mortality does not distinguish between death occurring from a prior healthy state, or death occurring from a prior disabled state. In LTC pricing, we need to isolate the mortality rate of healthy insureds i.e., only consider deaths occurring from a prior healthy state. In this paper, we analyze the impact of this error for the two forms of LTC coverage-the stand-alone LTC product and LTC as a rider to a life insurance product. The paper demonstrates the following: (a) how to create the theoretically correct mortality table for LTC pricing from a life insurance mortality table and mortality assumption for disabled lives; (b) a mathematical proof on the pricing impact of this error; and (c) some examples on the magnitude of this error for selected ages and different mortality assumptions for disabled lives.
Long term care (LTC) insurance is one of the hottest selling products in the insurance marketplac... more Long term care (LTC) insurance is one of the hottest selling products in the insurance marketplace. Pricing for long term care insurance is subject to significant risk due to the range of possible outcomes and resultant net costs that could be incurred with coverage. From a pricing perspective, this is characterized by a long tail in the pricing loss random variable. This means that traditional pricing which captures the mean of the pricing loss random variable, has a high risk of being inadequate and incurring significant losses. This paper addresses the issues of (a) conceptualizing the volatility risk using statistical confidence intervals; (b) developing a stochastic simulation model to quantity and price for this risk; and (c) use of a regression based methodology to price for this risk as a deterministic approximation to stochastic simulation. The results of this model are applied separately to the stand-alone LTC product and the LTC product as a rider to a life insurance policy. Besides demonstrating the effectiveness of the regression based approximation and the pricing significance of the volatility risk, we will also show the robustness of the volatility risk measure when actual experience factors are not fixed, but fluctuate uniformly around a range of values.
“Multivariate Analysis of Pension Plan Mortality Data”, Charles Vinsonhaler; Nalini Ravishanker; Jeyaraj Vadiveloo; Guy Rasoanaivo, April 2001
North American Actuarial Journal, 2001
This paper uses the logistic regression model to examine private pension plan data for 1989 -95 c... more This paper uses the logistic regression model to examine private pension plan data for 1989 -95 collected by the Retirement Plans Experience Committee of the Society of Actuaries. When only one explanatory variable, such as annuity class size, is used in modeling mortality rates, the model provides a reasonable fit to the data. Multiple explanatory variables give less satisfactory results.
Stochastic modeling of long-term care insurance
One of the key developments in modern actuarial science has been the introduction of stochastic m... more One of the key developments in modern actuarial science has been the introduction of stochastic models. This is necessitated by the design complexity of products being offered in today\u27s marketplace and the many variables that can impact the financial performance of these products. Most of the stochastic modeling has focused on the variables impacting the asset side of an insurance operation like market returns, asset defaults and interest rates. In this thesis, a stochastic model is developed for the Long-Term Care (LTC) Insurance product, focusing on the liability risks like mortality, morbidity and lapse behavior. The model tries to measure the pricing volatility of the LTC rider insurance as well as the stand-alone product, caused by the volatility in these liability risks. The stochastic process governing the model is a Markov Chain process. We first discuss the traditional deterministic pricing of Long-Term Care Insurance. An algorithm is proposed to simulate the LTC insurance process. In addition, a regression model is developed to estimate the volatility risk and thus derive a deterministic approximation to the risk-adjusted net single and level premiums. The robustness of the regression model is tested against the full simulation model, under different assumptions and changes in product design.
Long term care (LTC) pricing requires the use of underlying experience data of mortality and laps... more Long term care (LTC) pricing requires the use of underlying experience data of mortality and lapse rates of healthy insureds, LTC incidence rates, LTC utilization rates, and LTC termination rates from recovery or death. It is important that the pricing process uses the most up-to-date industry tables, modified for a company's own experience and anticipated changes due to improvements in medical science and technology. While companies have recognized this fact, less attention has been paid on the mortality of healthy insureds. It is customary for companies to use either industry experience tables or their own experience of mortality on life insurance in LTC pricing. Therein lies the subtle error that results: Life insurance mortality does not distinguish between death occurring from a prior healthy state, or death occurring from a prior disabled state. In LTC pricing, we need to isolate the mortality rate of healthy insureds i.e., only consider deaths occurring from a prior healthy state. In this paper, we analyze the impact of this error for the two forms of LTC coverage-the stand-alone LTC product and LTC as a rider to a life insurance product. The paper demonstrates the following: (a) how to create the theoretically correct mortality table for LTC pricing from a life insurance mortality table and mortality assumption for disabled lives; (b) a mathematical proof on the pricing impact of this error; and (c) some examples on the magnitude of this error for selected ages and different mortality assumptions for disabled lives.
Long term care (LTC) insurance is one of the hottest selling products in the insurance marketplac... more Long term care (LTC) insurance is one of the hottest selling products in the insurance marketplace. Pricing for long term care insurance is subject to significant risk due to the range of possible outcomes and resultant net costs that could be incurred with coverage. From a pricing perspective, this is characterized by a long tail in the pricing loss random variable. This means that traditional pricing which captures the mean of the pricing loss random variable, has a high risk of being inadequate and incurring significant losses. This paper addresses the issues of (a) conceptualizing the volatility risk using statistical confidence intervals; (b) developing a stochastic simulation model to quantity and price for this risk; and (c) use of a regression based methodology to price for this risk as a deterministic approximation to stochastic simulation. The results of this model are applied separately to the stand-alone LTC product and the LTC product as a rider to a life insurance policy. Besides demonstrating the effectiveness of the regression based approximation and the pricing significance of the volatility risk, we will also show the robustness of the volatility risk measure when actual experience factors are not fixed, but fluctuate uniformly around a range of values.