Valuing American Options Using Fast Recursive Projections

69 Pages Posted: 25 Jun 2012 Last revised: 7 Dec 2018

See all articles by Antonio Cosma

Antonio Cosma

Université du Luxembourg

Stefano Galluccio

BNP Paribas Fixed Income

Paola Pederzoli

University of Houston - C.T. Bauer College of Business

O. Scaillet

University of Geneva GSEM and GFRI; Swiss Finance Institute; University of Geneva - Research Center for Statistics

Date Written: March 2, 2016

Abstract

We introduce a fast and widely applicable numerical pricing method that uses recursive projections. We characterize its convergence speed. We find that the early exercise boundary of an American call option on a discrete dividend paying stock is higher under the Merton and Heston models than under the Black-Scholes model, as opposed to the continuous dividend case. A large database of call options on stocks with quarterly dividends shows that adding stochastic volatility and jumps to the Black-Scholes benchmark reduces the amount foregone by call holders failing to optimally exercise by 25\%. Transaction fees cannot fully explain the suboptimal behavior.

Keywords: Option pricing, American option, Bermudan option, discrete transform, discrete dividend paying stock, suboptimal non-exercise, numerical techniques

JEL Classification: G13, C63

Suggested Citation

Cosma, Antonio and Galluccio, Stefano and Pederzoli, Paola and Scaillet, Olivier, Valuing American Options Using Fast Recursive Projections (March 2, 2016). Available at SSRN: https://ssrn.com/abstract=2091236 or http://dx.doi.org/10.2139/ssrn.2091236

Antonio Cosma (Contact Author)

Université du Luxembourg ( email )

162a, avenue de la Faïencerie
Luxembourg, L-1511
Luxembourg
+352 46 66 44 6763 (Phone)
+352 46 66 44 6835 (Fax)

Stefano Galluccio

BNP Paribas Fixed Income ( email )

10, Harewood Avenue
NW1 6AA London
United Kingdom

Paola Pederzoli

University of Houston - C.T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

Olivier Scaillet

University of Geneva GSEM and GFRI ( email )

40 Boulevard du Pont d'Arve
Geneva 4, Geneva 1211
Switzerland
+ 41 22 379 88 16 (Phone)
+41 22 389 81 04 (Fax)

HOME PAGE: http://www.scaillet.ch

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

University of Geneva - Research Center for Statistics

Geneva
Switzerland

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