The Cross-Section of Asia-Pacific Mortality Dynamics: Implications for Longevity Risk Sharing

Biffis, Enrico; Lin, Yijia; Milidonis, Andreas

doi:10.2139/ssrn.2757364

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…This norm of coherence has been widely adopted in the literature. There are a range of extensions and applications of the common factor model, in terms of handling both sexes (Li, 2013;Li et al, 2016;Parr et al, 2016;Yang et al, 2016;Pitt et al, 2018;Wong et al, 2020), two hedging counterparties (Li and Hardy, 2011;Li and Luo, 2012;Li and Haberman, 2015;Lin and Tsai, 2016;Zhou and Li, 2017) or a few countries in the same geographic region (Biffis et al, 2017;Enchev et al, 2017;Chen and Millossovich, 2018) jointly.…”

Section: Introductionmentioning

confidence: 99%

A Double Common Factor Model for Mortality Projection Using Best-Performance Mortality Rates as Reference

Li¹,

Lee²,

Guthrie³

2021

ASTIN Bull.

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We construct a double common factor model for projecting the mortality of a population using as a reference the minimum death rate at each age among a large number of countries. In particular, the female and male minimum death rates, described as best-performance or best-practice rates, are first modelled by a common factor model structure with both common and sex-specific parameters. The differences between the death rates of the population under study and the best-performance rates are then modelled by another common factor model structure. An important result of using our proposed model is that the projected death rates of the population being considered are coherent with the projected best-performance rates in the long term, the latter of which serves as a very useful reference for the projection based on the collective experience of multiple countries. Our out-of-sample analysis shows that the new model has potential to outperform some conventional approaches in mortality projection.

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Section: Introductionmentioning

confidence: 99%

A Double Common Factor Model for Mortality Projection Using Best-Performance Mortality Rates as Reference

Li¹,

Lee²,

Guthrie³

2021

ASTIN Bull.

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“…Note that model (1.2) can be replaced by a more general time series model, although researchers often claim that a unit root AR(1) model fits well to real mortality rates. Some recent applications of the above models (1.1) and (1.2) with ρ = 1 in actuarial science include Li et al (2015), Kwok et al (2016), Enchev et al (2017), Biffis et al (2017), Lin et al (2017), Wong et al (2017), and Zhu et al (2017). Among these applications, a commonly employed statistical inference is the two-step procedure in Lee and Carter (1992), which first estimates α x , β x , k t for x = 1, .…”

Section: Introductionmentioning

confidence: 99%

Bias-Corrected Inference for a Modified Lee–carter Mortality Model

Liu¹,

Ling²,

Li³

et al. 2019

ASTIN Bull.

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As a benchmark mortality model in forecasting future mortality rates and hedging longevity risk, the widely employed Lee–Carter model (Lee, R.D. and Carter, L.R. (1992) Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659–671.) suffers from a restrictive constraint on the unobserved mortality index for ensuring model’s identification and a possible inconsistent inference. Recently, a modified Lee–Carter model (Liu, Q., Ling, C. and Peng, L. (2018) Statistical inference for Lee–Carter mortality model and corresponding forecasts. North American Actuarial Journal, to appear.) removes this constraint and a simple least squares estimation is consistent with a normal limit when the mortality index follows from a unit root or near unit root AR(1) model with a nonzero intercept. This paper proposes a bias-corrected estimator for this modified Lee–Carter model, which is consistent and has a normal limit regardless of the mortality index being a stationary or near unit root or unit root AR(1) process with a nonzero intercept. Applications to the US mortality rates and a simulation study are provided as well.

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“…Wei (2017) modelled the CBD mortality indexes with the LLCBD model proposed by Liu and Li (2017), and performed a further study on the counterparty credit risk concerning K-forwards. Biffis et al (2017) introduced a security that is written on the population-specific time-varying parameters in the augmented common factor model (Li and Lee, 2005). Although they name their security slightly differently ('k-forward' instead of 'K-forward'), the spirit behind (i.e., writing securities on the time-varying parameters in a stochastic mortality model) is essentially the same.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we study risk-neutral valuation of K-forwards and other securities written on parametric mortality indexes. This objective distinguishes our paper from the previous work on parametric mortality indexes by Biffis et al (2017) and Tan et al (2014), who ignored the cost of hedging completely. Third, we consider dynamic hedging with K-forwards and options, extending the work Tan et al (2014) who focused on static hedging and used forward contracts only.…”

Section: Introductionmentioning

confidence: 99%

Constructing Out-of-the-Money Longevity Hedges Using Parametric Mortality Indexes

Balasooriya

et al. 2019

North American Actuarial Journal

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Proposed by Chan et al. (2014), parametric mortality indexes (i.e., indexes created using the time-varying parameters in a suitable stochastic mortality model) can be used to develop tradable mortality-linked derivatives such as K-forwards. Compared to existing indexes such as the LLMA's LifeMetrics, parametric mortality indexes are richer in information content, allowing the market to better concentrate liquidity. In this paper, we further study this concept in several aspects. First, we consider options written on parametric mortality indexes. Such options enable hedgers to create out-of-the-money longevity hedges, which, compared to at-the-money-hedges created with q-/K-forwards, may better meet hedgers' need for protection against downside risk. Second, using the properties of the time-series processes for the parametric mortality indexes, we derive analytical risk-neutral pricing formulas for K-forwards and options. In addition to convenience, the analytical pricing formulas remove the need for computationally intensive nested simulations that are entailed in, for example, the calculation of the hedging instruments' values when a dynamic hedge is adjusted. Finally, we construct static and dynamic Greek hedging strategies using K-forwards and options, and demonstrate empirically the conditions under which an out-of-the-money hedge is more economically justifiable than an at-the-money one.

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The Cross-Section of Asia-Pacific Mortality Dynamics: Implications for Longevity Risk Sharing

Cited by 8 publications

References 34 publications

A Double Common Factor Model for Mortality Projection Using Best-Performance Mortality Rates as Reference

A Double Common Factor Model for Mortality Projection Using Best-Performance Mortality Rates as Reference

Bias-Corrected Inference for a Modified Lee–carter Mortality Model

Constructing Out-of-the-Money Longevity Hedges Using Parametric Mortality Indexes

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