A fast method for pricing American options under the variance gamma model
| Autor: | Fu, Weilong, Hirsa, Ali |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Journal of Computational Finance, 2021 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.21314/JCF.2021.002 |
| Popis: | We investigate methods for pricing American options under the variance gamma model. The variance gamma process is a pure jump process which is constructed by replacing the calendar time by the gamma time in a Brownian motion with drift, which makes it a time-changed Brownian motion. In general, the finite difference method and the simulation method can be used for pricing under this model, but their speed is not satisfactory. So there is a need for fast but accurate approximation methods. In the case of Black-Merton-Scholes model, there are fast approximation methods, but they cannot be utilized for the variance gamma model. We develop a new fast method inspired by the quadratic approximation method, while reducing the error by making use of a machine learning technique on pre-calculated quantities. We compare the performance of our proposed method with those of the existing methods and show that this method is efficient and accurate for practical use. Comment: 16 pages, 1 Figure, 4 Tables |
| Databáze: | arXiv |
| Externí odkaz: |
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