Under the Solvency II Directive, insurance and reinsurance undertakings are required to perform continuous monitoring of risks and market consistent valuation of assets and liabilities. Solvency II application is particularly demanding, both theoretically and under the computational point of view. At present, any technique able to improve on accuracy or to reduce computing time is highly desirable. This works reports initial results on the design of a Deep Learning Network, aimed to reduce computing time by avoiding the standard full nested Monte Carlo approach.

Tuning a Deep Learning Network for Solvency II: Preliminary Results

Ugo Fiore;Zelda Marino;Francesca Perla;SCOGNAMIGLIO, SALVATORE;Paolo Zanetti
2018

Abstract

Under the Solvency II Directive, insurance and reinsurance undertakings are required to perform continuous monitoring of risks and market consistent valuation of assets and liabilities. Solvency II application is particularly demanding, both theoretically and under the computational point of view. At present, any technique able to improve on accuracy or to reduce computing time is highly desirable. This works reports initial results on the design of a Deep Learning Network, aimed to reduce computing time by avoiding the standard full nested Monte Carlo approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/66303
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