Modeling investment guarantees in Japan: A risk-neutral GARCH approach

Ng, Andrew; Li, Johnny Siu-Hang; Chan, Wai-Sum

doi:10.1016/j.irfa.2010.12.001

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…Similar to the observation from Table 5, it is observed from Figures 2–4 that compared to the proposed model the BM and JDBM models underestimate the E(PV) of the three guarantees. Similar results are obtained by Ng et al, 39 who shows that the fee obtained with GBM model is lesser compared to the fee obtained with the GARCH model.…”

Section: Empirical Analysissupporting

confidence: 88%

“…The difference is smaller for a death benefit guarantee as premiums for death benefit guarantees are very low compared to living benefit guarantee premium. 39 We have already shown in Section 3.1, the benefit of CBD forecasted probabilities over actual probabilities. The impact of these probabilities on GMDB, GMAB, and GMIB contracts is given in Table 6.…”

Section: F I G U R Ementioning

confidence: 83%

“…Hence, a lower fee will be charged if we does not take long memory property of stock returns in consideration. The difference is smaller for a death benefit guarantee as premiums for death benefit guarantees are very low compared to living benefit guarantee premium 39 …”

Section: Empirical Analysismentioning

confidence: 96%

See 2 more Smart Citations

Variable annuities valuation under a mixed fractional Brownian motion environment with jumps considering mortality risk

Sharma

Selvamuthu

Natarajan

2022

Appl Stoch Models Bus & Ind

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Pricing a variable annuity (VA) is traditionally based on models with standard Brownian motion (BM). The assumption of independent increments in the BM is not always realistic as the price process of the risky assets exhibit a long-range dependency. To model such assets, a fractional Brownian motion (FBM) with Hurst parameter)is an appropriate model. However, due to arbitrage in an FBM model and jumps in stock returns, we consider in this work a mixed FBM (MFBM) model with jumps and evaluate variable annuities with different riders. We analyze the proposed model numerically to see the impact of mortality risk. We perform a comparison between eight stochastic models to obtain the best-suited mortality model, for a dataset of the US male population. Finally, we obtain the price of VA guarantees using the forecasted values from the fitted mortality model.

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Section: Empirical Analysissupporting

confidence: 88%

Section: F I G U R Ementioning

confidence: 83%

See 1 more Smart Citation

Variable annuities valuation under a mixed fractional Brownian motion environment with jumps considering mortality risk

Sharma

Selvamuthu

Natarajan

2022

Appl Stoch Models Bus & Ind

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“…Many standard criteria are used to select the appropriate model for the data. A risk‐neutral measure is obtained by following Siu‐Hang Li et al, 10 and Ng et al 11 for the proposed risky asset model. Moreover, we have considered the dynamic withdrawal strategy and surrender benefit.…”

Section: Introductionmentioning

confidence: 99%

Time series model for GLWB with surrender benefit and stochastic interest rate: Dynamic withdrawal approach

Singh

Arunachalam

Selvamuthu

2023

Appl Stoch Models Bus & Ind

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This study investigates the pricing problem of a variable annuity (VA) contract embedded with a guaranteed lifetime withdrawal benefit (GLWB) rider. VAs are annuities in which the value is linked to a bond and equity sub‐account fund. The guaranteed lifetime withdrawal benefit rider regularly provides a series of payments to the policyholder for the term of the policy while he/she is alive, regardless of portfolio performance. At the time of the policyholder's death, the remaining fund value is given to his nominee. Therefore, proper fund modeling is critical in the pricing of VA products. Several writers in the literature used a GBM model in which variance is considered to be constant to represent the fund value in a variable annuity contract. However, on the other hand, the returns on financial assets are non‐normally distributed in real life. A bit much Kurtosis, leverage effect, and Non‐zero Skewness characterize the returns. The generalized autoregressive conditional heteroscedastic (GARCH) models are also used for presenting a discrete framework for the pricing of GLWB. Still, the interest rate was kept constant without including the surrender benefit and the static withdrawal approach, which keeps the model far from the real scenario. Thus, in this research, the generalized GARCH models are used with surrender benefit and dynamic withdrawal strategy to develop a time series model for the pricing of annuity that overcomes the constraints of previous models. A numerical illustration and sensitivity analysis are used to examine the suggested model.

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“…The pricing of participating life insurance products with guarantees has been largely studied under the assumptions of the Black-Scholes option pricing model by Tiong [11], Milevsky and Posner [12], Lin et al [13], Ng et al [14], Gatzert and Holzmüller [15], and Fan [16]. However, when the historical prices of the underlying assets are analyzed and used as the basis for setting the pricing assumptions with respect to the risk model, it is possible to recognize that asset returns rarely meet the standard Brownian motion.…”

Section: Introductionmentioning

confidence: 99%

Valuation of Guaranteed Unitized Participating Life Insurance under MEGB2 Distribution

Zheng

Hao

Bai

et al. 2019

Discrete Dynamics in Nature and Society

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Crisis events have significantly changed the view that extreme events in financial markets have negligible probability. Especially in the life insurance market, the price of guaranteed participating life insurance contract will be affected by a change in asset volatility which leads to the fluctuations in embedded option value. Considering the correlation of different asset prices, MEGB2 (multivariate exponential generalized beta of the second kind) distribution is proposed to price guaranteed participating life insurance contract which can effectively describe the dependence structure of assets under some extreme risks. Assuming the returns of two different assets follow the MEGB2 distribution, a multifactor fair valuation pricing model of insurance contract is split into four components: the basic contract, the annual dividend option, the terminal dividend option, and the surrender option. This paper studies the effect of death rate, minimum guaranteed yield rate, annual dividend ratio, terminal dividend ratio, and surrender on the embedded option values and calculates the single premium of the insurance contract under different influence factors. The Least-Squares Monte Carlo simulation method is used to simulate the pricing model. This article makes a comparison in the sensitivity of the pricing parameters under the MEGB2 distribution and Multivariate Normal distribution asset returns. Finally, an optimal hedging strategy is designed to cover the possible risks of the underlying assets, which can effectively hedge the risks of portfolio.

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Modeling investment guarantees in Japan: A risk-neutral GARCH approach

Cited by 9 publications

References 21 publications

Variable annuities valuation under a mixed fractional Brownian motion environment with jumps considering mortality risk

Variable annuities valuation under a mixed fractional Brownian motion environment with jumps considering mortality risk

Time series model for GLWB with surrender benefit and stochastic interest rate: Dynamic withdrawal approach

Valuation of Guaranteed Unitized Participating Life Insurance under MEGB2 Distribution

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