A hybrid data mining framework for variable annuity portfolio valuation

DOI10.1017/ASB.2023.26zbMATH Open1545.91264MaRDI QIDQ6569739

Shu Li, Hyukjun Gweon

Publication date: 9 July 2024

Published in: ASTIN Bulletin (Search for Journal in Brave)

zbMATH Keywords

random forestvariable annuityportfolio valuationbootstrap aggregatinghybrid framework

Mathematics Subject Classification ID

Actuarial mathematics (91G05)

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets
• An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios
• Standard errors for bagged and random forest estimators
• Moment matching machine learning methods for risk management of large variable annuity portfolios
• Application of data clustering and machine learning in variable annuity valuation
• Batch mode active learning framework and its application on valuing large variable annuity portfolios
• Is it optimal to group policyholders by age, gender, and seniority for BEL computations based on model points?
• Quantifying uncertainty in random forests via confidence intervals and hypothesis tests
• Regression Modeling for the Valuation of Large Variable Annuity Portfolios
• Bias-corrected random forests in regression
• Data Clustering with Actuarial Applications
• AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS
• Random Forests and Adaptive Nearest Neighbors
• Variable annuity pricing, valuation, and risk management: a survey
• Random forests

This page was built for publication: A hybrid data mining framework for variable annuity portfolio valuation

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6569739)