Least-Squares Monte Carlo for Proxy Modeling in Life Insurance: Neural Networks
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References listed on IDEAS
- Anne-Sophie Krah & Zoran Nikolić & Ralf Korn, 2018. "A Least-Squares Monte Carlo Framework in Proxy Modeling of Life Insurance Companies," Risks, MDPI, vol. 6(2), pages 1-26, June.
- Mark Kiermayer & Christian Wei{ss}, 2019. "Grouping of Contracts in Insurance using Neural Networks," Papers 1912.09964, arXiv.org.
- Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
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Cited by:
- Giulia Di Nunno & Anton Yurchenko-Tytarenko, 2022. "Sandwiched Volterra Volatility model: Markovian approximations and hedging," Papers 2209.13054, arXiv.org, revised Jul 2024.
- Vali Asimit & Ioannis Kyriakou & Jens Perch Nielsen, 2020. "Special Issue “Machine Learning in Insurance”," Risks, MDPI, vol. 8(2), pages 1-2, May.
- Nelson Kemboi Yego & Juma Kasozi & Joseph Nkurunziza, 2021. "A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya," Data, MDPI, vol. 6(11), pages 1-17, November.
- Borgonovo, Emanuele & Clemente, Gian Paolo & Rabitti, Giovanni, 2024. "Why insurance regulators need to require sensitivity settings of internal models for their approval," Finance Research Letters, Elsevier, vol. 60(C).
- Shuai Yang & Kenneth Q. Zhou, 2023. "On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach," Risks, MDPI, vol. 11(12), pages 1-18, November.
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Keywords
least-squares Monte Carlo method; proxy modeling; life insurance; Solvency II; neural networks; machine learning; ensemble method;All these keywords.
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