Skip to main content
Kent Academic Repository

Discrete Algorithms for Multivariate Financial Calculus

Tunaru, Radu (2010) Discrete Algorithms for Multivariate Financial Calculus. In: Crisan, Dan, ed. Stochastic Analysis. Springer, pp. 243-266. ISBN 978-3-642-15357-0. (doi:10.1007/978-3-642-15358-7_12) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:28647)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.1007/978-3-642-15358-7_12

Abstract

Quantitative financial calculus is dominated by calculations of integrals related to various moments of probability distributions used for modelling. Here, we develop a general technique that facilitates the numerical calculations of options, prices for the difficult case of mult-assets, for the majority of European payoff contracts. The algorithms proposed here rely on known weak convergence results, hence making use of the gaussian probability kernel even when modelling with non-gaussian distributions. In addition, this technique can be employed for calculating greek parameter. We prove that the weak convergence characterizing condition can still be applied under some mild assumption on the payoff function of financial options.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-642-15358-7_12
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Depositing User: Catherine Norman
Date Deposited: 02 Feb 2012 16:26 UTC
Last Modified: 05 Nov 2024 10:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/28647 (The current URI for this page, for reference purposes)

University of Kent Author Information

Tunaru, Radu.

Creator's ORCID:
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.