Risk measurement of a guaranteed annuity option under a stochastic modelling framework

Huan, Gao; Mamon, Rogemar; Liu, Xiaoming

doi:10.1016/j.matcom.2016.07.003

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Indeed, the liability issue is essentially an asset liability issue; thus, the liability distribution estimation depends on risk management decision. Examples of modeling the loss random variable for a product with an option-embedded feature are illustrated in Gao et al (2017) and Zhao et al (2018). In risk management, there are two well-known approaches for VAs: (i) the "actuarial" approach that uses the distribution of the guarantee liabilities discounted at the risk-free rate of interest and (ii) the dynamic-hedging approach that uses financial engineering and assumes that a portfolio of bounds and stocks will replicate the guarantee payoff.…”

Section: Introductionmentioning

confidence: 99%

An Effective Bias-Corrected Bagging Method for the Valuation of Large Variable Annuity Portfolios

Gweon

Mamon

2020

ASTIN Bull.

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To evaluate a large portfolio of variable annuity (VA) contracts, many insurance companies rely on Monte Carlo simulation, which is computationally intensive. To address this computational challenge, machine learning techniques have been adopted in recent years to estimate the fair market values (FMVs) of a large number of contracts. It is shown that bootstrapped aggregation (bagging), one of the most popular machine learning algorithms, performs well in valuing VA contracts using related attributes. In this article, we highlight the presence of prediction bias of bagging and use the bias-corrected (BC) bagging approach to reduce the bias and thus improve the predictive performance. Experimental results demonstrate the effectiveness of BC bagging as compared with bagging, boosting, and model points in terms of prediction accuracy.

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Section: Introductionmentioning

confidence: 99%