Skip navigation

A distributed algorithm for European options with nonlinear volatility

A distributed algorithm for European options with nonlinear volatility

Lai, C.-H. ORCID: 0000-0002-7558-6398 , Parrott, A.K., Rout, S. and Honnor, M.E. (2005) A distributed algorithm for European options with nonlinear volatility. Computers & Mathematics with Applications, 49 (5-6). pp. 885-894. ISSN 0898-1221 (doi:https://doi.org/10.1016/j.camwa.2004.03.014)

[img]
Preview
PDF
(item_909)_LAI_PARROTT_ROUT_2005.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (644kB)

Abstract

A distributed algorithm is developed to solve nonlinear Black-Scholes equations in the hedging of portfolios. The algorithm is based on an approximate inverse Laplace transform and is particularly suitable for problems that do not require detailed knowledge of each intermediate time steps.

Item Type: Article
Additional Information: [1] Received March 2004, Accepted March 2004, Available online 13 May 2005.
Uncontrolled Keywords: option pricing, Black-Scholes equation, finite-difference schemes, nonlinear volatility 1. INTRODUCTION
Subjects: Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Mechanics & Reliability Group
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Science & Engineering Group
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Science
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:
Last Modified: 15 Oct 2016 07:08
URI: http://gala.gre.ac.uk/id/eprint/909

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics