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http://hdl.handle.net/2445/120997
Title: | On the data-driven COS method |
Author: | Leitao, Alvaro Oosterlee, C. W. (Cornelis W.) Ortiz Gracia, Luis Bohte, Sander M. |
Keywords: | Estadística matemàtica Anàlisi de Fourier Matemàtica aplicada Mètode de Montecarlo Mathematical statistics Fourier analysis Applied mathematics Monte Carlo method |
Issue Date: | 2018 |
Publisher: | Elsevier B.V. |
Abstract: | In this paper, we present the data-driven COS method, ddCOS. It is a Fourier-based financial option valuation method which assumes the availability of asset data samples: a characteristic function of the underlying asset probability density function is not required. As such, the presented technique represents a generalization of the well-known COS method [1]. The convergence of the proposed method is in line with Monte Carlo methods for pricing financial derivatives. The ddCOS method is then particularly interesting for density recovery and also for the efficient computation of the option's sensitivities Delta and Gamma. These are often used in risk management, and can be obtained at a higher accuracy with ddCOS than with plain Monte Carlo methods. |
Note: | Versió postprint del document publicat a: https://doi.org/10.1016/j.amc.2017.09.002 |
It is part of: | Applied Mathematics and Computation, 2018, vol. 317, num. January, p. 68-84 |
URI: | http://hdl.handle.net/2445/120997 |
Related resource: | https://doi.org/10.1016/j.amc.2017.09.002 |
ISSN: | 0096-3003 |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
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