An approximation of small-time probability density functions in a general jump diffusion model

Le Zhang and Wolfgang M. Schmidt

Applied Mathematics and Computation, 2016, vol. 273, issue C, 741-758

Abstract: We propose a method for approximating probability density functions related to multidimensional jump diffusion processes. For small-time horizons, a closed-form approximation of the characteristic function is derived based on the Itô–Taylor expansion. The probability density function is then approximated numerically by inverting the characteristic function using fast Fourier transform. As application we consider a general stochastic volatility model, which involves time-/state-dependent drift and diffusion functions as well as jump components. We test our approach under the Heston model and the Bates model and show that our method provides accurate approximations.

Keywords: Jump diffusion process; Itô–Taylor expansions; Stochastic volatility models; Characteristic functions; Probability density functions (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300315013351
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:273:y:2016:i:c:p:741-758

DOI: 10.1016/j.amc.2015.10.003

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().