Please use this identifier to cite or link to this item:
Title: A novel SVM option pricing model & an intelligent ATM option straddle trading system
Authors: He, Jianxin.
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2011
Abstract: Options are very popular financial derivatives that allow investors to control their investment risks in the securities market. Determining the theoretical price for an option, or option pricing, is regarded as one of the most important issues in financial research; a number of parametric and nonparametric option pricing approaches have been researched and developed. In this study, we want to propose a novel option pricing model incorporating both the parametric and nonparametric methods. The parametric methods give rough approximations of the current option price, and the nonparametric support vector machine (SVM) focuses on capturing and reducing the error residuals in the approximations to provide more accurate option valuation. Together with the popularity of options, the option straddle has also become an important investment strategy to capitalize on the uncertainties of the underlying asset prices. The trading of straddles, especially the at-the-money straddles, is usually thought of as volatility trading. However, apart from the volatility, the fluctuation in the prices of the straddles is also an important factor to be taken into account for straddle trading. This study proposes an at-the-money option straddle trading system which not only capitalizes on the uncertainties of the underlying asset prices but also profits and hedges risks from capturing the fluctuations in the prices of the straddles.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
1.02 MBAdobe PDFView/Open

Page view(s) 50

checked on Sep 23, 2020

Download(s) 50

checked on Sep 23, 2020

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.