Artikel

Using neural networks to price and hedge variable annuity guarantees

This paper explores the use of neural networks to reduce the computational cost of pricing and hedging variable annuity guarantees. Pricing these guarantees can take a considerable amount of time because of the large number of Monte Carlo simulations that are required for the fair value of these liabilities to converge. This computational requirement worsens when Greeks must be calculated to hedge the liabilities of these guarantees. A feedforward neural network is a universal function approximator that is proposed as a useful machine learning technique to interpolate between previously calculated values and avoid running a full simulation to obtain a value for the liabilities. We propose methodologies utilizing neural networks for both the tasks of pricing as well as hedging four different varieties of variable annuity guarantees. We demonstrated a significant efficiency gain using neural networks in this manner. We also experimented with different error functions in the training of the neural networks and examined the resulting changes in network performance.

Sprache
Englisch

Erschienen in
Journal: Risks ; ISSN: 2227-9091 ; Volume: 7 ; Year: 2019 ; Issue: 1 ; Pages: 1-19 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
variable annuities
GMxB
hedging
neural networks

Ereignis
Geistige Schöpfung
(wer)
Doyle, Daniel
Groendyke, Chris
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2019

DOI
doi:10.3390/risks7010001
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Doyle, Daniel
  • Groendyke, Chris
  • MDPI

Entstanden

  • 2019

Ähnliche Objekte (12)