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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Bravo, J. M. (2026). Pricing e-forwards: An Investigation Using Bayesian Model Ensembles and Stacking Regression. In Rocha et al. (Ed.), Proceedings of 19th Iberian Conference on Information Systems and Technologies (CISTI 2024). CISTI 2024. Lecture Notes in Networks and Systems, vol 1751. Springer, Cham. (pp. 413-427). Berlin: Springer.
Exportar Referência (IEEE) 
J. M. Bravo,  "Pricing e-forwards: An Investigation Using Bayesian Model Ensembles and Stacking Regression", in Proc. of 19th Iberian Conf. on Information Systems and Technologies (CISTI 2024). CISTI 2024. Lecture Notes in Networks and Systems, vol 1751. Springer, Cham, Rocha et al., Ed., Berlin, Springer, 2026, vol. 1751, pp. 413-427
Exportar BibTeX 
@incollection{bravo2026_1772846893597,
	author = "Bravo, J. M.",
	title = "Pricing e-forwards: An Investigation Using Bayesian Model Ensembles and Stacking Regression",
	chapter = "",
	booktitle = "Proceedings of 19th Iberian Conference on Information Systems and Technologies (CISTI 2024). CISTI 2024. Lecture Notes in Networks and Systems, vol 1751. Springer, Cham",
	year = "2026",
	volume = "1751",
	series = "Lecture Notes in Networks and Systems",
	edition = "",
	pages = "413-413",
	publisher = "Springer",
	address = "Berlin",
	url = "https://link.springer.com/chapter/10.1007/978-3-032-12882-9_36"
}
Exportar RIS 
TY  - CHAP
TI  - Pricing e-forwards: An Investigation Using Bayesian Model Ensembles and Stacking Regression
T2  - Proceedings of 19th Iberian Conference on Information Systems and Technologies (CISTI 2024). CISTI 2024. Lecture Notes in Networks and Systems, vol 1751. Springer, Cham
VL  - 1751
AU  - Bravo, J. M.
PY  - 2026
SP  - 413-427
DO  - 10.1007/978-3-032-12882-9_36
CY  - Berlin
UR  - https://link.springer.com/chapter/10.1007/978-3-032-12882-9_36
AB  - For pension plans and annuity providers, capital market-based solutions represent one potential approach to managing excessive mortality and longevity risk exposures. The valuation of longevity-linked securities necessitates the application of stochastic methods to project future mortality developments, alongside an incomplete market premium principle that accounts for the market price of longevity risk. This paper examines the influence of the mortality model, process and parameter uncertainty, and the choice of premium principle on the market pricing of e-forward (life expectancy) contracts. Two model combination approaches for mortality forecasting are evaluated—Bayesian Model Ensemble and Stacking Regression with the elastic net as the meta-learner—alongside two widely used pricing principles: the Wang transform and the Proportional Hazard transform. Mortality and life annuity data for the total Portuguese population are employed to calibrate the models and generate illustrative empirical results.
ER  -