Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/122335
Title: | A dimension reduction Shannon-wavelet based method for option pricing |
Author: | Dang, Duy-Minh Ortiz Gracia, Luis |
Keywords: | Anàlisi de Fourier Sistemes estocàstics Anàlisi financera Fourier analysis Stochastic systems Investment analysis |
Issue Date: | May-2018 |
Publisher: | Springer Science + Business Media |
Abstract: | We present a robust and highly efficient dimension reduction Shannon-wavelet method for computing European option prices and hedging parameters under a general jump-diffusion model with square-root stochastic variance and multi-factor Gaussian interest rates. Within a dimension reduction framework, the option price can be expressed as a two-dimensional integral that involves only (i) the value of the variance at the terminal time, and (ii) the time-integrated variance process conditional on this value. A Shannon wavelet inverse Fourier technique is developed to approximate the conditional density of the time-integrated variance process. Furthermore, thanks to the excellent approximation properties of Shannon wavelets, the overall pricing procedure is reduced to the evaluation of just a single integral that involves only the density of the terminal variance value. This single integral can be accurately evaluated, since the density of the variance at the terminal time is known in closed-form. We develop sharp approximation error bounds for the option price and hedging parameters. Numerical experiments confirm the robustness and impressive efficiency of the method. |
Note: | Versió postprint del document publicat a: https://link.springer.com/article/10.1007/s10915-017-0556-y |
It is part of: | Journal of Scientific Computing, 2018, vol. 75, num. 2, p. 733-761 |
URI: | http://hdl.handle.net/2445/122335 |
Related resource: | https://doi.org/10.1007/s10915-017-0556-y |
ISSN: | 0885-7474 |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
678687.pdf | 556.73 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.