Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


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Re-Published in
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(A Community of about 10.000.000 Academics)


Editorial Advisory Board's Chair
William Lesso

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org

 

Editorial Advisory Board

Journal's Reviewers
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology


A Proposed Model for Tracking the University Interdisciplinary Projects
Rafael Melgarejo, Paulina Cadena
(pages: 1-2)

Enhancing Teaching, Adaptability and Presentation Skills through Improvisational Theater
Thomas J. Marlowe
(pages: 3-7)

Interprofessional Collaborative Practice to Improve Patient Outcomes: A Pilot Study
Jennifer Styron, Catherine Dearman, Sheila Whitworth, Henrietta Brown
(pages: 8-13)

Using a Common Pedagogy across Multiple Disciplines to Improve Student Learning
Ronald A. Styron, Jr., Jennifer L. Styron
(pages: 14-16)

Enhancing Writing through Strengthened Executive Function
Russell Jay Hendel
(pages: 17-22)

Using Interdisciplinary and Active Research to Encourage Higher Resolution Research and Prototyping in Design
Adream Blair-Early, Frankie Flood
(pages: 23-27)

Integration of Education: Using Social Media Networks to Engage Students
Risa Blair, Tina M. Serafini
(pages: 28-31)

Management, Resources and Reproductive Biology
Bernard Wallner, Martin Fieder
(pages: 32-33)

Effectiveness and Utility of a Case-Based Model for Delivering Engineering Ethics Professional Development Units
Heidi Ann Hahn
(pages: 34-39)

Informing via Research: Methods, Challenges and Success when Using a Multi-Disciplinary Team and Reverse Engineering Analysis Processes to Answer a 200 Year Old Question
Melinda H. Connor, Sallyanne Payton
(pages: 40-47)


 

Abstracts

 


ABSTRACT


Monte Carlo Simulation of an American Option

Gikiri Thuo


We implement gradient estimation techniques for sensitivity analysis of option pricing which can be efficiently employed in Monte Carlo simulation. Using these techniques we can simultaneously obtain an estimate of the option value together with the estimates of sensitivities of the option value to various parameters of the model. After deriving the gradient estimates we incorporate them in an iterative stochastic approximation algorithm for pricing an option with early exercise features. We illustrate the procedure using an example of an American call option with a single dividend that is analytically tractable. In particular we incorporate estimates for the gradient with respect to the early exercise threshold level.

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