Details
| Original language | English |
|---|---|
| Pages (from-to) | 419-432 |
| Number of pages | 14 |
| Journal | Journal of forecasting |
| Volume | 33 |
| Issue number | 6 |
| Publication status | Published - 27 Aug 2014 |
Abstract
High-frequency trading and automated algorithm impose high requirements on computational methods. We provide a model-free option pricing approach with neural networks, which can be applied to real-time pricing and hedging of FX options. In contrast to well-known theoretical models, an essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of real-time input variables. To test its ability for the purpose of high-frequency trading, we perform an empirical run-time trading simulation with a tick dataset of EUR/USD options on currency futures of 4 weeks. In very short non-overlapping 15-minute out-of-sample intervals, theoretical option prices derived from the Black model compete against nonparametric option prices through two different neural network topologies. We show that the approximated pricing function of learning networks is suitable for generating fast run-time option pricing evaluation as their performance is slightly better in comparison to theoretical prices. The derivation of the network function is also useful for performing hedging strategies. We conclude that the performance of closed-form pricing models depends highly on the volatility estimator, whereas neural networks can avoid this estimation problem but require market liquidity for training. Nevertheless, we also have to take particular enhancements into account, which give us useful hints for further research and steps.
Keywords
- black model, delta-hedging, high-frequency data, neural networks, option pricing
ASJC Scopus subject areas
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Science Applications
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
- Decision Sciences(all)
- Management Science and Operations Research
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In: Journal of forecasting, Vol. 33, No. 6, 27.08.2014, p. 419-432.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Real-time pricing and hedging of options on currency futures with artificial neural networks
AU - Von Spreckelsen, Christian
AU - Von Mettenheim, Hans Jörg
AU - Breitner, Michael H.
N1 - Copyright: Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014/8/27
Y1 - 2014/8/27
N2 - High-frequency trading and automated algorithm impose high requirements on computational methods. We provide a model-free option pricing approach with neural networks, which can be applied to real-time pricing and hedging of FX options. In contrast to well-known theoretical models, an essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of real-time input variables. To test its ability for the purpose of high-frequency trading, we perform an empirical run-time trading simulation with a tick dataset of EUR/USD options on currency futures of 4 weeks. In very short non-overlapping 15-minute out-of-sample intervals, theoretical option prices derived from the Black model compete against nonparametric option prices through two different neural network topologies. We show that the approximated pricing function of learning networks is suitable for generating fast run-time option pricing evaluation as their performance is slightly better in comparison to theoretical prices. The derivation of the network function is also useful for performing hedging strategies. We conclude that the performance of closed-form pricing models depends highly on the volatility estimator, whereas neural networks can avoid this estimation problem but require market liquidity for training. Nevertheless, we also have to take particular enhancements into account, which give us useful hints for further research and steps.
AB - High-frequency trading and automated algorithm impose high requirements on computational methods. We provide a model-free option pricing approach with neural networks, which can be applied to real-time pricing and hedging of FX options. In contrast to well-known theoretical models, an essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of real-time input variables. To test its ability for the purpose of high-frequency trading, we perform an empirical run-time trading simulation with a tick dataset of EUR/USD options on currency futures of 4 weeks. In very short non-overlapping 15-minute out-of-sample intervals, theoretical option prices derived from the Black model compete against nonparametric option prices through two different neural network topologies. We show that the approximated pricing function of learning networks is suitable for generating fast run-time option pricing evaluation as their performance is slightly better in comparison to theoretical prices. The derivation of the network function is also useful for performing hedging strategies. We conclude that the performance of closed-form pricing models depends highly on the volatility estimator, whereas neural networks can avoid this estimation problem but require market liquidity for training. Nevertheless, we also have to take particular enhancements into account, which give us useful hints for further research and steps.
KW - black model
KW - delta-hedging
KW - high-frequency data
KW - neural networks
KW - option pricing
UR - http://www.scopus.com/inward/record.url?scp=84906817764&partnerID=8YFLogxK
U2 - 10.1002/for.2311
DO - 10.1002/for.2311
M3 - Article
AN - SCOPUS:84906817764
VL - 33
SP - 419
EP - 432
JO - Journal of forecasting
JF - Journal of forecasting
SN - 0277-6693
IS - 6
ER -