Efficient importance sampling for a credit risk model
(English)Manuscript (preprint) (Other academic)
This paper considers importance sampling for estimation of rare-event probabilities in a Markovian intensity model for credit risk. The main contribution is the design of efficient importance sampling algorithms using subsolutions of a certain Hamilton-Jacobi equation. For certain instances of the credit risk model the proposed algorithm is proved to be asymptotically optimal. The computational gain compared to standard Monte Carlo is illustrated by numerical experiments.
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:kth:diva-144420OAI: oai:DiVA.org:kth-144420DiVA: diva2:713463
QS 20142014-04-232014-04-232014-04-24Bibliographically approved