JDOI variance reduction method and the pricing of American-style options
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This article revisits the Diffusion Operator Integral (DOI) variance reduction technique originally proposed in Heath and Platen (2002) and extends its theoretical concept to the pricing of American-style options under (time-homogeneous) Lévy stochastic differential equations. The resulting Jump Diffusion Operator Integral (JDOI) method can be combined with numerous Monte Carlo-based stopping-time algorithms, including the ubiquitous least-squares Monte Carlo (LSMC) algorithm of Longstaff and Schwartz (cf. Carriere (1996) and Longstaff and Schwartz (2001)). We exemplify the usefulness of our theoretical derivations under a concrete, though very general jump-diffusion stochastic volatility dynamics and test the resulting LSMC-based version of the JDOI method. The results provide evidence of a strong variance reduction when compared with a simple application of the LSMC algorithm and proves that applying our technique on top of Monte Carlo-based pricing schemes provides a powerful way to speed-up these methods.
Originalsprog | Engelsk |
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Tidsskrift | Quantitative Finance |
Vol/bind | 22 |
Udgave nummer | 4 |
Sider (fra-til) | 639-656 |
ISSN | 1469-7688 |
DOI | |
Status | Udgivet - 2022 |
- Det Natur- og Biovidenskabelige Fakultet
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