A genetic programming approach for delta hedging
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Yin:GPEM,
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author = "Zheng Yin and Anthony Brabazon and
Conall O'Sullivan and Philip A. Hamill",
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title = "A genetic programming approach for delta hedging",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2019",
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volume = "20",
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number = "1",
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pages = "67--92",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Hedging,
Delta neutrality",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-018-9334-3",
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size = "26 pages",
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abstract = "In this paper, using high-frequency intra-daily data
from the UK market, we employ genetic programming (GP)
to uncover a hedging strategy for FTSE 100 call
options, hedged using FTSE 100 futures contracts. The
output from the evolved strategies is a rebalancing
signal which is conditioned upon a range of dynamic
non-linear factors related to market conditions
including liquidity and volatility. When this signal
exceeds threshold values during the trading day, the
hedge position is rebalanced. The performance of the
GP-evolved strategy is evaluated against a number of
commonly used, time-based, deterministic hedging
strategies where the hedge position is rebalanced at
fixed time intervals ranging from 5 minutes to one day.
Assuming the delta hedger pays the bid-ask spread on
the futures contract whenever the portfolio is
rebalanced, this study finds that the GP-evolved
hedging strategy out-performs standard, deterministic,
time-based approaches. Empirical analysis shows that
the superior performance of the GP strategy is driven
by its ability to account for non-linear intra-day
persistence in high frequency measures of liquidity and
volatility. This study is the first to apply a GP
methodology for the task of delta hedging with high
frequency data, a significant risk management issue for
investors and market makers in financial options.",
- }
Genetic Programming entries for
Zheng Yin
Anthony Brabazon
Conall O'Sullivan
Philip A Hamill
Citations