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- Title
Adjoint-based Monte Carlo calibration of financial market models.
- Authors
Kaebe, C.; Maruhn, J. H.; Sachs, E. W.
- Abstract
Adjoint methods have recently gained considerable importance in the finance sector, because they allow to quickly compute option sensitivities with respect to a large number of model parameters. In this paper we investigate how the efficiency of adjoint methods can be exploited to speed up the Monte Carlo-based calibration of financial market models. After analyzing the calibration problem both theoretically and numerically, we derive the associated adjoint equation and propose its application in combination with a multi-layer method, for which we prove convergence to a stationary point of the underlying optimization problem. Detailed numerical examples illustrate the performance of the method. In particular, the proposed algorithm reduces the calibration time for a typical equity market model with time-dependent model parameters from over three hours to less than ten minutes on a usual desktop PC.
- Subjects
MONTE Carlo method; MATHEMATICAL optimization; MATHEMATICAL models; ALGORITHMS; STOCK exchanges
- Publication
Finance & Stochastics, 2009, Vol 13, Issue 3, p351
- ISSN
0949-2984
- Publication type
Article
- DOI
10.1007/s00780-009-0097-9