Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 223-238 |
| Seitenumfang | 16 |
| Fachzeitschrift | Int. J. Appl. Decis. Sci. |
| Jahrgang | 7 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 2014 |
Abstract
In this paper, we present steps towards a model-driven financial decision support system (FDSS) to pricing options on currency futures, which can be embedded in a high-frequency trading process. Due to the difficulty of option valuation, we provide an alternative heuristic option pricing approach with neural networks. We show that the use of neural networks is not only suitable in generating accurate trading signals, but also in generating automated fast run-time trading signals for the decision taker. To achieve this, we conduct an experiment with an empirical tick data set of EUR/USD options on currency futures of four weeks. An essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of input variables. Nevertheless, we also have to take particular limitations into account, which give us useful hints for further research and steps.
ASJC Scopus Sachgebiete
- Volkswirtschaftslehre, Ökonometrie und Finanzen (insg.)
- Volkswirtschaftslehre und Ökonometrie
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Strategie und Management
- Entscheidungswissenschaften (insg.)
- Managementlehre und Operations Resarch
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
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in: Int. J. Appl. Decis. Sci., Jahrgang 7, Nr. 3, 2014, S. 223-238.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Steps towards a high-frequency financial decision support system to pricing options on currency futures with neural networks.
AU - Spreckelsen, Christian von
AU - Mettenheim, Hans-Jörg von
AU - Breitner, Michael H.
N1 - Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - In this paper, we present steps towards a model-driven financial decision support system (FDSS) to pricing options on currency futures, which can be embedded in a high-frequency trading process. Due to the difficulty of option valuation, we provide an alternative heuristic option pricing approach with neural networks. We show that the use of neural networks is not only suitable in generating accurate trading signals, but also in generating automated fast run-time trading signals for the decision taker. To achieve this, we conduct an experiment with an empirical tick data set of EUR/USD options on currency futures of four weeks. An essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of input variables. Nevertheless, we also have to take particular limitations into account, which give us useful hints for further research and steps.
AB - In this paper, we present steps towards a model-driven financial decision support system (FDSS) to pricing options on currency futures, which can be embedded in a high-frequency trading process. Due to the difficulty of option valuation, we provide an alternative heuristic option pricing approach with neural networks. We show that the use of neural networks is not only suitable in generating accurate trading signals, but also in generating automated fast run-time trading signals for the decision taker. To achieve this, we conduct an experiment with an empirical tick data set of EUR/USD options on currency futures of four weeks. An essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of input variables. Nevertheless, we also have to take particular limitations into account, which give us useful hints for further research and steps.
KW - Design science
KW - FDSS
KW - Financial decision support system
KW - High-frequency data
KW - Neural networks
KW - Option pricing
KW - Trading systems
UR - http://www.scopus.com/inward/record.url?scp=84903946112&partnerID=8YFLogxK
U2 - 10.1504/IJADS.2014.063228
DO - 10.1504/IJADS.2014.063228
M3 - Article
VL - 7
SP - 223
EP - 238
JO - Int. J. Appl. Decis. Sci.
JF - Int. J. Appl. Decis. Sci.
IS - 3
ER -