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

Fiore U.;
2018-01-01

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.
2018
9783319898230
9783319898247
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4898215
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact