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Monte Carlo Methods and Applications

Managing Editor: Sabelfeld, Karl K.

Editorial Board: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis


CiteScore 2018: 0.66

SCImago Journal Rank (SJR) 2018: 0.319
Source Normalized Impact per Paper (SNIP) 2018: 0.720

Mathematical Citation Quotient (MCQ) 2018: 0.18

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1569-3961
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Volume 24, Issue 1

Issues

Pricing barrier options in the Heston model using the Heath–Platen estimator

Sema Coskun / Ralf KornORCID iD: http://orcid.org/0000-0002-9123-3883
Published Online: 2018-02-01 | DOI: https://doi.org/10.1515/mcma-2018-0004

Abstract

Both barrier options and the Heston stochastic volatility model are omnipresent in real-life applications of financial mathematics. In this paper, we apply the Heath–Platen (HP) estimator (as first introduced by Heath and Platen in [12]) to price barrier options in the Heston model setting as an alternative to conventional Monte Carlo methods and PDE based methods. We demonstrate the superior performance of the HP estimator via numerical examples and explain this performance by a detailed look at the underlying theoretical concept of the HP estimator.

Keywords: Barrier option pricing; Heston stochastic volatility model; Heath–Platen estimator

MSC 2010: 91G60

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About the article

Received: 2017-08-31

Accepted: 2018-01-06

Published Online: 2018-02-01

Published in Print: 2018-03-01


This work was supported by the DFG-research training group 1932 “Stochastic models for innovations in the engineering sciences” of which Sema Coskun has been a doctoral researcher and Ralf Korn is the spokesperson. Both gratefully acknowledge the support of the DFG. The contents of this work is based on parts of the first author’s dissertation at the Department of Mathematics of TU Kaiserslautern.


Citation Information: Monte Carlo Methods and Applications, Volume 24, Issue 1, Pages 29–41, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/mcma-2018-0004.

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