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dc.contributor.authorDang, Ouen_US
dc.contributor.authorFeng, Mingbinen_US
dc.contributor.authorHardy, Mary R.en_US
dc.identifier.citationDang, O., Feng, M. & Hardy, M. R. (2023). Two-stage nested simulation of tail risk measurement: a likelihood ratio approach. Insurance: Mathematics and Economics, 108, 1-24.
dc.description.abstractEstimating tail risk measures is an important task in many financial and actuarial applications and often requires nested simulations, with the outer simulations representing real world scenarios, and the inner simulations typically used for risk neutral pricing or conditional risk measurement. The standard nested simulation method is highly flexible, able to incorporate complex asset models and exotic payoff structures, but it is also computationally highly burdensome, particularly in a multi-period setting, when inner simulation paths are required at each time step of each outer level scenario. In this study, we propose and analyze a two-stage simulation procedure that efficiently estimates the conditional tail expectation of cost of a dynamic hedging program for a Variable Annuity Guaranteed Minimum Withdrawal Benefit (GMWB), under a multi-period nested simulation. In each of the two stages, the method re-uses the same set of inner level simulation paths for each outer scenario at each time point, using a likelihood ratio method to re-weight the probabilities of each individual path for the different outer scenarios. Our numerical study shows that our two-stage, likelihood ratio weighted procedure can offer a very significant improvement in efficiency, of the order of 95% as measured by the RMSE, compared with a standard nested simulation with the same computational cost.en_US
dc.relation.ispartofInsurance: Mathematics and Economicsen_US
dc.rights© 2022 Elsevier B.V. All rights reserved.en_US
dc.subjectBusiness::Finance::Risk managementen_US
dc.titleTwo-stage nested simulation of tail risk measurement: a likelihood ratio approachen_US
dc.typeJournal Articleen
dc.contributor.schoolNanyang Business Schoolen_US
dc.subject.keywordsNested Simulationen_US
dc.subject.keywordsLikelihood Ratio Methoden_US
dc.description.acknowledgementWe acknowledge the support of the Natural Sciences and Engineering Research Council of Canada, funding reference number 03754 (Hardy) and 03755 (Feng). This work was also supported by the Society of Actuaries through a Center of Actuarial Excellence Research Grant and through the Hickman Scholarship held by Ou Dang. Ou Dang has also received support from the Ontario Graduate Scholarship.en_US
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