Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Marx, Brian D. & Eilers, Paul H. C., 1998. "Direct generalized additive modeling with penalized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 28(2), pages 193-209, August.
- Magnus, Jan R., 1978.
"Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix,"
Journal of Econometrics, Elsevier, vol. 7(3), pages 281-312, April.
- Magnus, Jan, 1977. "Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix," University of Amsterdam, Actuarial Science and Econometrics Archive 293034, University of Amsterdam, Faculty of Economics and Business.
- Magnus, J.R., 1978. "Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix," Other publications TiSEM 388c2c25-0925-4b56-834a-7, Tilburg University, School of Economics and Management.
- S. N. Wood, 2000. "Modelling and smoothing parameter estimation with multiple quadratic penalties," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 413-428.
- 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.
- Simon N. Wood & Zheyuan Li & Gavin Shaddick & Nicole H. Augustin, 2017. "Generalized Additive Models for Gigadata: Modeling the U.K. Black Smoke Network Daily Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1199-1210, July.
- Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
- Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
- Weiß Christian & Nikolić Zoran, 2019. "An aspect of optimal regression design for LSMC," Monte Carlo Methods and Applications, De Gruyter, vol. 25(4), pages 283-290, December.
- Giampiero Marra & Simon N. Wood, 2012. "Coverage Properties of Confidence Intervals for Generalized Additive Model Components," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(1), pages 53-74, March.
- Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
- Simon N. Wood & Yannig Goude & Simon Shaw, 2015. "Generalized additive models for large data sets," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(1), pages 139-155, January.
- Michael B. Gordy & Sandeep Juneja, 2010.
"Nested Simulation in Portfolio Risk Measurement,"
Management Science, INFORMS, vol. 56(10), pages 1833-1848, October.
- Michael B. Gordy & Sandeep Juneja, 2008. "Nested simulation in portfolio risk measurement," Finance and Economics Discussion Series 2008-21, Board of Governors of the Federal Reserve System (U.S.).
- Bauer, Daniel & Reuss, Andreas & Singer, Daniela, 2012. "On the Calculation of the Solvency Capital Requirement Based on Nested Simulations," ASTIN Bulletin, Cambridge University Press, vol. 42(2), pages 453-499, November.
- Christophe Dutang, 2017. "Some explanations about the IWLS algorithm to fit generalized linear models," Working Papers hal-01577698, HAL.
- Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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.
- 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).
- E. Lorenzo & G. Piscopo & M. Sibillo, 2024. "Addressing the economic and demographic complexity via a neural network approach: risk measures for reverse mortgages," Computational Management Science, Springer, vol. 21(1), pages 1-22, June.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Anne-Sophie Krah & Zoran Nikoli'c & Ralf Korn, 2019. "Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies," Papers 1909.02182, arXiv.org.
- Belitz, Christiane & Lang, Stefan, 2008. "Simultaneous selection of variables and smoothing parameters in structured additive regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 61-81, September.
- Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014.
"Modeling structural change in the European metropolitan areas during the process of economic integration,"
Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
- Christian Longhi & Antonio Musolesi & Catherine Baumont, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Post-Print halshs-01228053, HAL.
- Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
- Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1540-1551, April.
- Aur'elien Alfonsi & Bernard Lapeyre & J'er^ome Lelong, 2022. "How many inner simulations to compute conditional expectations with least-square Monte Carlo?," Papers 2209.04153, arXiv.org, revised May 2023.
- Simon N. Wood & Zheyuan Li & Gavin Shaddick & Nicole H. Augustin, 2017. "Generalized Additive Models for Gigadata: Modeling the U.K. Black Smoke Network Daily Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1199-1210, July.
- Musolesi Antonio & Mazzanti Massimiliano, 2014.
"Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries,"
Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 521-541, December.
- Mazzanti, M. & Musolesi, A., 2013. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," Working Papers 2013-08, Grenoble Applied Economics Laboratory (GAEL).
- Antonio Musolesi & Massimiliano Mazzanti, 2014. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic developement relation for advanced countries," Post-Print hal-01123027, HAL.
- Massimiliano Mazzanti & Antonio Musolesi, 2014. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," SEEDS Working Papers 2214, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Aug 2014.
- Aurélien Alfonsi & Bernard Lapeyre & Jérôme Lelong, 2022. "How many inner simulations to compute conditional expectations with least-square Monte Carlo?," Working Papers hal-03770051, HAL.
- Aurélien Alfonsi & Bernard Lapeyre & Jérôme Lelong, 2023. "How many inner simulations to compute conditional expectations with least-square Monte Carlo?," Post-Print hal-03770051, HAL.
- Sylvie Charlot & Riccardo Crescenzi & Antonio Musolesi, 2014. "Augmented and Unconstrained: revisiting the Regional Knowledge Production Function," SEEDS Working Papers 2414, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Aug 2014.
- Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
- Hongjun Ha & Daniel Bauer, 2022. "A least-squares Monte Carlo approach to the estimation of enterprise risk," Finance and Stochastics, Springer, vol. 26(3), pages 417-459, July.
- Baccini, Michela & Biggeri, Annibale & Lagazio, Corrado & Lertxundi, Aitana & Saez, Marc, 2007. "Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on health," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4324-4336, May.
- Aurélien Alfonsi & Bernard Lapeyre & Jérôme Lelong, 2023. "How Many Inner Simulations to Compute Conditional Expectations with Least-square Monte Carlo?," Methodology and Computing in Applied Probability, Springer, vol. 25(3), pages 1-25, September.
- Alfonsi, Aurélien & Cherchali, Adel & Infante Acevedo, Jose Arturo, 2021. "Multilevel Monte-Carlo for computing the SCR with the standard formula and other stress tests," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 234-260.
- Mazzanti, Massimiliano & Musolesi, Antonio, 2013.
"Nonlinearity, Heterogeneity and Unobserved Effects in the CO2-income Relation for Advanced Countries,"
Climate Change and Sustainable Development
162374, Fondazione Eni Enrico Mattei (FEEM).
- Massimiliano Mazzanti & Antonio Musolesi, 2013. "Nonlinearity, Heterogeneity and Unobserved Effects in the CO2-income Relation for Advanced Countries," Working Papers 2013.91, Fondazione Eni Enrico Mattei.
- Aur'elien Alfonsi & Adel Cherchali & Jose Arturo Infante Acevedo, 2020. "Multilevel Monte-Carlo for computing the SCR with the standard formula and other stress tests," Papers 2010.12651, arXiv.org, revised Apr 2021.
- Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
- Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
More about this item
Keywords
least-squares monte carlo method; machine learning; proxy modeling; life insurance; Solvency II;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:8:y:2020:i:1:p:21-:d:323720. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.