Details
| Original language | English |
|---|---|
| Pages | 466-477 |
| Number of pages | 12 |
| Publication status | Published - 2007 |
| Event | 15th European Conference on Information Systems, ECIS 2007 - St. Gallen, Switzerland Duration: 7 Jun 2007 → 9 Jun 2007 |
Conference
| Conference | 15th European Conference on Information Systems, ECIS 2007 |
|---|---|
| Country/Territory | Switzerland |
| City | St. Gallen |
| Period | 7 Jun 2007 → 9 Jun 2007 |
Abstract
Today's theoretic, i.e. stochastic, option valuation models, inherently base on unrealistic assumptions. Especially the adequate estimation of a volatility measure is discussed controversially. Several studies show that the issuer's individual volatility measure is used to exploit the consumer surplus. Moreover, not only the well-known inputs but also hidden inputs affect option market prices significantly. Therefore alternative options pricing approaches use artificial neural networks to learn market prices models from observed market data. The core question is: Are time-varying artificial neural networks (ANN) based on a high amount of automatically collected market data more accurate and more realistic than classical methods like, e. g., the ones of Black/Scholes or Cox/Ross/Rubinstein? To answer this question the software suite WARRANT-PRO-1 is used which incorporates the web mining agent PISA (Partially Intelligent Software Agent) and the neurosimulator FAUN (Fast Approximation with Universal Neural Networks). In real-time WARRANT-PRO-1 synthesizes ANN market valuation functions of Internet-available data or other (semi-)structured text sources. FAUN trains ANN, e. g., market prices for standard and user defined so called OTC (over the counter) options. Statistical analyses and examples including German DAX warrants and OTC options presented in this article indicate the feasibility of this approach.
Keywords
- Artificial neural networks, Black/Scholes model, Derivatives, Market price models, Options, Web mining
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
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2007. 466-477 Paper presented at 15th European Conference on Information Systems, ECIS 2007, St. Gallen, Switzerland.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Real-time market valuation of options based on web mining and neurosimulation
AU - Bartels, Patrick
AU - Breitner, Michael
N1 - Copyright: Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Today's theoretic, i.e. stochastic, option valuation models, inherently base on unrealistic assumptions. Especially the adequate estimation of a volatility measure is discussed controversially. Several studies show that the issuer's individual volatility measure is used to exploit the consumer surplus. Moreover, not only the well-known inputs but also hidden inputs affect option market prices significantly. Therefore alternative options pricing approaches use artificial neural networks to learn market prices models from observed market data. The core question is: Are time-varying artificial neural networks (ANN) based on a high amount of automatically collected market data more accurate and more realistic than classical methods like, e. g., the ones of Black/Scholes or Cox/Ross/Rubinstein? To answer this question the software suite WARRANT-PRO-1 is used which incorporates the web mining agent PISA (Partially Intelligent Software Agent) and the neurosimulator FAUN (Fast Approximation with Universal Neural Networks). In real-time WARRANT-PRO-1 synthesizes ANN market valuation functions of Internet-available data or other (semi-)structured text sources. FAUN trains ANN, e. g., market prices for standard and user defined so called OTC (over the counter) options. Statistical analyses and examples including German DAX warrants and OTC options presented in this article indicate the feasibility of this approach.
AB - Today's theoretic, i.e. stochastic, option valuation models, inherently base on unrealistic assumptions. Especially the adequate estimation of a volatility measure is discussed controversially. Several studies show that the issuer's individual volatility measure is used to exploit the consumer surplus. Moreover, not only the well-known inputs but also hidden inputs affect option market prices significantly. Therefore alternative options pricing approaches use artificial neural networks to learn market prices models from observed market data. The core question is: Are time-varying artificial neural networks (ANN) based on a high amount of automatically collected market data more accurate and more realistic than classical methods like, e. g., the ones of Black/Scholes or Cox/Ross/Rubinstein? To answer this question the software suite WARRANT-PRO-1 is used which incorporates the web mining agent PISA (Partially Intelligent Software Agent) and the neurosimulator FAUN (Fast Approximation with Universal Neural Networks). In real-time WARRANT-PRO-1 synthesizes ANN market valuation functions of Internet-available data or other (semi-)structured text sources. FAUN trains ANN, e. g., market prices for standard and user defined so called OTC (over the counter) options. Statistical analyses and examples including German DAX warrants and OTC options presented in this article indicate the feasibility of this approach.
KW - Artificial neural networks
KW - Black/Scholes model
KW - Derivatives
KW - Market price models
KW - Options
KW - Web mining
UR - http://www.scopus.com/inward/record.url?scp=84869410837&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:84869410837
SP - 466
EP - 477
T2 - 15th European Conference on Information Systems, ECIS 2007
Y2 - 7 June 2007 through 9 June 2007
ER -