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Mortality Modeling With Non‐Gaussian Innovations and Applications to the Valuation of Longevity Swaps
Author(s) -
Wang ChouWen,
Huang HongChih,
Liu IChien
Publication year - 2013
Publication title -
journal of risk and insurance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.055
H-Index - 63
eISSN - 1539-6975
pISSN - 0022-4367
DOI - 10.1111/j.1539-6975.2013.12002.x
Subject(s) - inverse gaussian distribution , econometrics , gaussian , statistics , longevity risk , mathematics , futures contract , swap (finance) , gaussian network model , interest rate swap , economics , financial economics , finance , physics , pension , mathematical analysis , distribution (mathematics) , quantum mechanics
A BSTRACT This article provides an iterative fitting algorithm to generate maximum likelihood estimates under the Cox regression model and employs non‐Gaussian distributions—the jump diffusion (JD), variance gamma (VG), and normal inverse Gaussian (NIG) distributions—to model the error terms of the Renshaw and Haberman ([Renshaw, A. E., 2006]) (RH) model. In terms of mean absolute percentage error, the RH model with non‐Gaussian innovations provides better mortality projections, using 1900–2009 mortality data from England and Wales, France, and Italy. Finally, the lower hedge costs of longevity swaps according to the RH model with non‐Gaussian innovations are not only based on the lower swap curves implied by the best prediction model, but also in terms of the fatter tails of the unexpected losses it generates.