title: |
Warrants Price forecasting using kernel machine and EKF-ANN: a comparative study |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-01-7 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/jcis.2006.99 (how to use a DOI) | |
author(s): |
Hsing-Wen Wang, JIAN-HONG WANG, TSE-PING DONG, SHENG-HSUN HSU |
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publication date: |
October 2006 |
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keywords: |
Black-Scholes, SVM, GARCH, ANFIS, Derivatives |
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abstract: |
The Black-Scholes options pricing model (BSM) is limited by the influences of many unexpected real world phenomena caused due to its six unreasonable assumptions, which often make the miss-pricing result because of the difference of market convention in practical. If we were to soundly take these phenomena into account, the pricing error could be reduced. In this paper, we try to make a comparative study between the support vector machines (SVM) and the Extended Kalman Filters-based Artificial Neural Networks, named adaptive neural-based fuzzy inference system (ANFIS). The performance indicates the SVM method is better than the ANFIS. Using evidence from the warrants market in Taiwan, it helps to provide an alternative way to refine the options valuation. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |