A two-decrement model for the valuation and risk measurement of a guaranteed annuity option

Zhao, Yixing; Mamon, Rogemar; Huan, Gao

doi:10.1016/j.ecosta.2018.06.004

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Our construction of the numerical and analytic approximations underlines a commitment to financial practice and addresses the implementation concerns of industrial end users. New developments in the analysis and valuation of long-term insurance contracts with correlated risk factors described by ATSMs (e.g., [37] and [38]) as well as dynamic investment protection plan [17] could benefit further from the introduction of regime-switching features of this paper.…”

Section: 2mentioning

confidence: 99%

Bond pricing formulas for Markov-modulated affine term structure models

Rodrigo¹,

Mamon²

2021

JIMO

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This article provides new developments in characterizing the class of regime-switching exponential affine interest rate processes in the context of pricing a zero-coupon bond. A finite-state Markov chain in continuous time dictates the random switching of time-dependent parameters of such processes. We present exact and approximate bond pricing formulas by solving a system of partial differential equations and minimizing an error functional. The bond price expression exhibits a representation that shows how it is explicitly impacted by the rate matrix and the time-dependent coefficient functions of the short rate models. We validate the bond pricing formulas numerically by examining a regime-switching Vasicek model.

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Section: 2mentioning

confidence: 99%

Bond pricing formulas for Markov-modulated affine term structure models

Rodrigo¹,

Mamon²

2021

JIMO

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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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