A Note on Monte Carlo Greeks using the Characteristic Function

27 Pages Posted: 27 Nov 2008 Last revised: 30 Nov 2008

See all articles by Joerg Kienitz

Joerg Kienitz

University of Cape Town; University of Wuppertal - Applied Mathematics

Date Written: November 28, 2008

Abstract

We consider the derivation of generic Monte Carlo estimators for Greeks for (path-dependent) options with discontinuous payoffs in the case where only the characteristic function is known. In Kienitz (2008) we have shown how to derive such Greeks for a wide range of models under the assumption that the transition probability is known in closed form. Unfortunately, this is not always the case. For example when considering exponential Levy models with stochastic volatility such as the Variance Gamma model with a Gamma Ornstein-Uhlenbeck or CIR stochastic clock. The characteristic function in this case the density is only given through its characteristic function. We give an algorithm to compute the probability density from the characteristic function and show that computing the transition density in this way gives the same results as in Kienitz (2008) but works for very general models. In this paper we focus on the Variance Gamma model and the same model with a Gamma Ornstein-Uhlenbeck stochastic clock. Since the methods are very general we can cope with other complex models like the Normal Inverse Gaussian model, considering other types of stochastic clocks or other classes of models where the characteristic function is known.

Keywords: Monte Carlo, Greeks, Levy, Characterisitc Function, Fourier Transform, Proxy

Suggested Citation

Kienitz, Joerg, A Note on Monte Carlo Greeks using the Characteristic Function (November 28, 2008). Available at SSRN: https://ssrn.com/abstract=1307605 or http://dx.doi.org/10.2139/ssrn.1307605

Joerg Kienitz (Contact Author)

University of Cape Town ( email )

South Africa

University of Wuppertal - Applied Mathematics ( email )

Gaußstraße 20
42097 Wuppertal
Germany

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
262
Abstract Views
1,027
rank
121,155
PlumX Metrics