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A Refined Binomial Lattice for Pricing American Asian Options

A Refined Binomial Lattice for Pricing American Asian Options We present simple and fast algorithms for computing very tight upper and lower bounds on the prices of American Asian options in the binomial model. We introduce a new refined version of the Cox-Ross-Rubinstein (1979) binomial lattice of stock prices. Each node in the lattice is partitioned into ‘nodelets’, each of which represents all paths arriving at the node with a specific geometric stock price average. The upper bound uses an interpolation idea similar to the Hull-White (1993) method. From the backward-recursive upper-bound computation, we estimate a good exercise rule that is consistent with the refined lattice. This exercise rule is used to obtain a lower bound on the option price using a modification of a conditional-expectation based idea from Rogers-Shi (1995) and Chalasani-Jha-Varikooty (1998). Our algorithms run in time proportional to the number of nodelets in the refined lattice, which is smaller than n4/20 for n > 14 periods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Derivatives Research Springer Journals

A Refined Binomial Lattice for Pricing American Asian Options

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References (23)

Publisher
Springer Journals
Copyright
Copyright © 1999 by Kluwer Academic Publishers
Subject
Finance; Investments and Securities
ISSN
1380-6645
eISSN
1573-7144
DOI
10.1023/A:1009622231124
Publisher site
See Article on Publisher Site

Abstract

We present simple and fast algorithms for computing very tight upper and lower bounds on the prices of American Asian options in the binomial model. We introduce a new refined version of the Cox-Ross-Rubinstein (1979) binomial lattice of stock prices. Each node in the lattice is partitioned into ‘nodelets’, each of which represents all paths arriving at the node with a specific geometric stock price average. The upper bound uses an interpolation idea similar to the Hull-White (1993) method. From the backward-recursive upper-bound computation, we estimate a good exercise rule that is consistent with the refined lattice. This exercise rule is used to obtain a lower bound on the option price using a modification of a conditional-expectation based idea from Rogers-Shi (1995) and Chalasani-Jha-Varikooty (1998). Our algorithms run in time proportional to the number of nodelets in the refined lattice, which is smaller than n4/20 for n > 14 periods.

Journal

Review of Derivatives ResearchSpringer Journals

Published: Oct 14, 2004

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