A Modified Least-Squares Simulation Approach to Value American Barrier Options

Lihua Zhang (), Weiguo Zhang (), Weijun Xu () and Xiang Shi ()

Computational Economics, 2014, vol. 44, issue 4, 489-506

Abstract: This paper presents the total least squares quasi-Monte Carlo approach (TLSQM) for valuing American barrier options, which modifies the least-squares Monte Carlo method (LSM). The total least squares are applied to estimate the conditional expected payoff to the option holder from continuation, and the quasi-Monte Carlo method is used to simulate the paths of stock prices. We show that the approximations of a set of conditional expectation functions in the TLSQM method converge to the true expectation function under general assumptions. It provides the mathematical foundation for the use of the TLSQM method in the derivatives research. In order to illustrate the high efficiency of the proposed approach, several examples are given to price American barrier options using TLSQM, LSM, and binomial trees, respectively. The numerical analysis demonstrates that the TLSQM method is accurate, and it makes improvements in computational speed and efficiency, compared with the LSM method. Copyright Springer Science+Business Media New York 2014

Keywords: Least-squares simulation; Total least squares; Quasi-Monte Carlo; Barrier option (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s10614-013-9409-4 (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:kap:compec:v:44:y:2014:i:4:p:489-506

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-013-9409-4

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().