Monte Carlo methods for pricing discrete Parisian options

Carole Bernard and Phelim Boyle

The European Journal of Finance, 2011, vol. 17, issue 3, 169-196

Abstract: The paper develops an efficient Monte Carlo method to price discretely monitored Parisian options based on a control variate approach. The paper also modifies the Parisian option design by assuming the option is exercised when the barrier condition is met rather than at maturity. We obtain formulas for this new design when the underlying is continuously monitored and develop an efficient Monte Carlo method for the discrete case. Our method can also be used for the case of multiple barriers. We use numerical examples to illustrate the approach and reveal important features of the different types of options considered. Some performance-based executive stock options include different tranches of discretely monitored Parisian options and we illustrate this with a practical example.

Keywords: Parisian options; Monte Carlo; discrete monitoring; control variate; early exercise; executive stock options (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/13518470903448473 (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:eurjfi:v:17:y:2011:i:3:p:169-196

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/REJF20

DOI: 10.1080/13518470903448473

Access Statistics for this article

The European Journal of Finance is currently edited by Chris Adcock

More articles in The European Journal of Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().