A genetic programming approach for delta hedging

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@Article{Yin:GPEM,
  author =       "Zheng Yin and Anthony Brabazon and 
                 Conall O'Sullivan and Philip A. Hamill",
  title =        "A genetic programming approach for delta hedging",
  journal =      "Genetic Programming and Evolvable Machines",
  note =         "Online first",
  keywords =     "genetic algorithms, genetic programming, Hedging,
                 Delta neutrality",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-018-9334-3",
  size =         "26 pages",
  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