EconPapers    
Economics at your fingertips  
 

The principle of not feeling the boundary for the SABR model

Nan Chen and Nian Yang

Quantitative Finance, 2019, vol. 19, issue 3, 427-436

Abstract: The stochastic alpha–beta–rho (SABR) model is widely used in fixed income and foreign exchange markets as a benchmark. The underlying process may hit zero with a positive probability and therefore an absorbing boundary at zero should be specified to avoid arbitrage opportunities. However, a variety of numerical methods choose to ignore the boundary condition to maintain the tractability. This paper develops a new principle of not feeling the boundary to quantify the impact of this boundary condition on the distribution of underlying prices. It shows that the probability of the SABR hitting zero decays to 0 exponentially as the time horizon shrinks. Applying this principle, we further show that conditional on the volatility process, the distribution of the underlying process can be approximated by that of a time-changed Bessel process with an exponentially negligible error. This discovery provides a theoretical justification for many almost exact simulation algorithms for the SABR model in the literature. Numerical experiments are also presented to support our results.

Date: 2019
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2018.1486037 (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:quantf:v:19:y:2019:i:3:p:427-436

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

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

 
Page updated 2019-04-07
Handle: RePEc:taf:quantf:v:19:y:2019:i:3:p:427-436