
Deep Importance Sampling
Virrion, Benjamin (2020), Deep Importance Sampling. https://basepub.dauphine.fr/handle/123456789/21160
Type
Document de travail / Working paperExternal document link
https://hal.archives-ouvertes.fr/hal-02887331Date
2020Series title
Cahier de recherche du CEREMADEPages
47
Metadata
Show full item recordAbstract (EN)
We present a generic path-dependent importance sampling algorithm where the Girsanov induced change of probability on the path space is represented by a sequence of neural networks taking the past of the trajectory as an input. At each learning step, the neural networks' parameters are trained so as to reduce the variance of the Monte Carlo estimator induced by this change of measure. This allows for a generic path dependent change of measure which can be used to reduce the variance of any path-dependent financial payoff. We show in our numerical experiments that for payoffs consisting of either a call, an asymmetric combination of calls and puts, a symmetric combination of calls and puts, a multi coupon autocall or a single coupon autocall, we are able to reduce the variance of the Monte Carlo estimators by factors between 2 and 9. The numerical experiments also show that the method is very robust to changes in the parameter values, which means that in practice, the training can be done offline and only updated on a weekly basis.Subjects / Keywords
Path-Dependence; Importance Sampling; Neural NetworksRelated items
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