This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Implied Binomial Trees

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Mark Rubinstein.
Abstract

Despite its success, the Black-Scholes formula has become increasingly unreliable over time in the very markets where one would expect it to be most accurate. In addition, attempts by financial economists to extract probabilistic information from option prices have been puny in comparison to what is clearly possible. This paper develops a new method for inferring risk-neutral probabilities (or state- contingent prices) from the simultaneously observed prices of European options. These probabilities are then used to infer a unique fully specified recombining binomial tree that is consistent with these probabilities (and hence consistent with all the observed option prices). If specified exogenously, the model can also accommodate local interest rates and underlying asset payout rates which are general functions of the concurrent underlying asset price and time. In a 200 step lattice, for example, there are a total of 60,301 unknowns: 40,200 potentially different move sizes, 20,100 potentially different move probabilities, and 1 interest rate to be determined from 60,301 independent equations, many of which are non-linear in the unknowns. Despite this, a backwards recursive solution procedure exists which is only slightly more time-consuming than for a standard binomial tree with given constant move sizes and move probabilities. Moreover, closed-form expressions exist for the values and hedging parameters of European options maturing with or before the end of the tree. The tree can also be used to value and hedge American and several types of exotic options. Interpreted in terms of continuous-time diffusion processes, the model here assumes that the drift and local volatility are at most functions of the underlying asset price and time. But instead of beginning with a parameterization of these functions (as in previous research), the model derives these functions endogenously to fit current option prices. As a result, it can be thought of as an attempt to exhaust the potential for single state-variable path-independent diffusion processes to rectify problems with the Black- Scholes formula that arise in practice.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.jstor.org/
File Format: text/html
File Function: link to document
Download Restriction: no

Publisher Info
Paper provided by University of California at Berkeley in its series Research Program in Finance Working Papers with number RPF-232.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 01 Jan 1994
Date of revision:
Handle: RePEc:ucb:calbrf:rpf-232

Contact details of provider:
Postal: University of California at Berkeley, Berkeley, CA USA
Phone: 510-642-0822
Fax: 510-642-6615
Email:
Web page: http://haas.berkeley.edu/finance/WP/rpflist.html
More information through EDIRC

Order Information:
Postal: IBER, F502 Haas Building, University of California at Berkeley, Berkeley CA 94720-1922
Email:

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords:

Other versions of this item:

This paper has been announced in the following NEP Reports: Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.
Statistics
Access and download statistics

Did you know? The RePEc project started in 1997. Its precursor, NetEc, dates back to 1993.

This page was last updated on 2009-10-31.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.