Numerical Methods for Non-Linear Black-Scholes Equations
Pascal Heider
Applied Mathematical Finance, 2010, vol. 17, issue 1, 59-81
Abstract:
In recent years non-linear Black-Scholes models have been used to build transaction costs, market liquidity or volatility uncertainty into the classical Black-Scholes concept. In this article we discuss the applicability of implicit numerical schemes for the valuation of contingent claims in these models. It is possible to derive sufficient conditions, which are required to ensure the convergence of the backward differentiation formula (BDF) and Crank-Nicolson scheme (CN) scheme to the unique viscosity solution. These stability conditions can be checked a priori and convergent schemes can be constructed for a large class of non-linear models and payoff profiles. However, if these conditions are not satisfied we show that the schemes are not convergent or produce spurious solutions. We study the practical implications of the derived stability criterions on relevant numerical examples.
Keywords: Non-linear Black-Scholes equation; BDF methods; fully implicit; viscosity solution (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/13504860903075670 (text/html)
Access to full text is restricted to subscribers.
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:taf:apmtfi:v:17:y:2010:i:1:p:59-81
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAMF20
DOI: 10.1080/13504860903075670
Access Statistics for this article
Applied Mathematical Finance is currently edited by Professor Ben Hambly and Christoph Reisinger
More articles in Applied Mathematical Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().