Implementing de-biased estimators using mixed sequences

Polala Arun Kumar () and Ökten Giray ()
Additional contact information
Polala Arun Kumar: Department of Mathematics, Florida State University, TallahasseeFL 32306-4510, USA
Ökten Giray: Department of Mathematics, Florida State University, TallahasseeFL 32306-4510, USA

Monte Carlo Methods and Applications, 2020, vol. 26, issue 4, 293-301

Abstract: We describe an implementation of the de-biased estimator using mixed sequences; these are sequences obtained from pseudorandom and low-discrepancy sequences. We use this implementation to numerically solve some stochastic differential equations from computational finance. The mixed sequences, when combined with Brownian bridge or principal component analysis constructions, offer convergence rates significantly better than the Monte Carlo implementation.

Keywords: De-biased estimator; low-discrepancy sequences; mixed sequences (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/mcma-2020-2075 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:mcmeap:v:26:y:2020:i:4:p:293-301:n:5

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html

DOI: 10.1515/mcma-2020-2075

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().