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| Title: | A hybrid option pricing model using a neural network for estimating volatility |
| Author (s): | Amornwattana, Sunisa Enke, David L. Dagli, Cihan H. |
| Department/Lab Affiliations: | Engineering Management & Systems Engineering Smart Engineering Systems Lab |
| Keywords: | Black-Scholes Hybrid Model Non-Linear Systems option pricing |
| Subject Terms: | Neural networks (Computer science) |
| Issue Date: | 2007 |
| Publisher: | Taylor & Francis |
| Citation: | Amornwattana, Sunisa, David Enke, and Cihan H. Dagli. "A Hybrid Options Pricing Model Using a Neural Network for Estimating Volatility.", International Journal of General Systems, Vol. 36, No. 5, 2007. |
| Abstract: | The Black-Scholes (BS) model is the standard approach used for pricing financial options. However, although being theoretically strong, option prices valued by the model often differ from the prices observed in the financial markets. This paper applies a hybrid neural network which preprocesses financial input data for improving the estimation of option market prices. This model is comprised of two parts. The first part is a neural network developed to estimate volatility. The second part is an additional neural network developed to value the difference between the BS model results and the actual market option prices. The resulting option price is then a summation between the BS model and the network response. The hybrid system with a neural network for estimating volatility provides better performance in terms of pricing accuracy than either the BS model with historical volatility (HV), or the BS model with volatility valued by the neural network. |
| Type: | Article - Journal text |
| In Title: | International Journal of General Systems |
| Copyright Notice: | Pre-print: author can archive; Post-print: author can archive with restrictions;Restriction: 12 month embargo for STM Journals;18 month embargo for SSH journals; Conditions: Some individual journals may have policies prohibiting pre-print archiving;Publisher's version/PDF cannot be used;On a non-profit server;Published source must be acknowledged;Must link to publisher version; This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: |
| Publisher URL: | |
| Link to this page: |
| title | A hybrid option pricing model using a neural network for estimating volatility |
| contributor.author | Amornwattana, Sunisa |
| contributor.author | Enke, David L. |
| contributor.author | Dagli, Cihan H. |
| contributor.deptlab | Engineering Management & Systems Engineering |
| contributor.deptlab | Smart Engineering Systems Lab |
| subject | Black-Scholes |
| subject | Hybrid Model |
| subject | Non-Linear Systems |
| subject | option pricing |
| subject.LCSH | Neural networks (Computer science) |
| date.issued | 2007 |
| publisher | Taylor & Francis |
| identifier.citation | Amornwattana, Sunisa, David Enke, and Cihan H. Dagli. "A Hybrid Options Pricing Model Using a Neural Network for Estimating Volatility.", International Journal of General Systems, Vol. 36, No. 5, 2007. |
| identifier.pub.URI | |
| description.abstract | The Black-Scholes (BS) model is the standard approach used for pricing financial options. However, although being theoretically strong, option prices valued by the model often differ from the prices observed in the financial markets. This paper applies a hybrid neural network which preprocesses financial input data for improving the estimation of option market prices. This model is comprised of two parts. The first part is a neural network developed to estimate volatility. The second part is an additional neural network developed to value the difference between the BS model results and the actual market option prices. The resulting option price is then a summation between the BS model and the network response. The hybrid system with a neural network for estimating volatility provides better performance in terms of pricing accuracy than either the BS model with historical volatility (HV), or the BS model with volatility valued by the neural network. |
| type | Article - Journal |
| type.DCMIType | text |
| rights | Pre-print: author can archive; Post-print: author can archive with restrictions;Restriction: 12 month embargo for STM Journals;18 month embargo for SSH journals; Conditions: Some individual journals may have policies prohibiting pre-print archiving;Publisher's version/PDF cannot be used;On a non-profit server;Published source must be acknowledged;Must link to publisher version; |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights.URI | |
| relation.isPartOf | International Journal of General Systems |
| date.available | 2008-07-17T15:55:19Z |
| identifier.persist.URI |