A Genetic Programming Approach for Delta Hedging

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{yin:agpafdh:cec2015,
  author =       "Zheng Yin and Anthony Brabazon and 
                 Conall O'Sullivan and Michael O'Neill",
  title =        "A Genetic Programming Approach for Delta Hedging",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  editor =       "Yadahiko Murata",
  pages =        "3312--3318",
  year =         "2015",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257304",
  abstract =     "Effective hedging of derivative securities is of
                 paramount importance to derivatives investors and to
                 market makers. The standard approach used to hedge
                 derivative instruments is delta hedging. In a
                 Black-Scholes setting, a continuously rebalanced delta
                 hedged portfolio will result in a perfect hedge with no
                 associated hedging error. In reality, continuous
                 rehedging is impossible and this raises the important
                 practical question such as when should a portfolio
                 manager rebalance the portfolio? In practice, many
                 portfolio managers employ relatively simple
                 deterministic rebalancing strategies, such as
                 rebalancing at uniform time intervals, or rehedging
                 when the underlying asset moves by a fixed number of
                 ticks. While such strategies are easy to implement they
                 will expose the portfolio to hedging risk, both in
                 terms of timing and also as the strategies do not
                 adequately consider market conditions. In this study we
                 propose a rebalancing trigger based on the output from
                 a GP-evolved hedging strategy that rebalances the
                 portfolio based on dynamic non-linear factors related
                 to the condition of the market, derived from the
                 theoretical literature, including a number of liquidity
                 and volatility factors. The developed GP-evolved
                 hedging strategy outperforms the deterministic time
                 based hedging methods when tested on FTSE 100 call
                 options. This paper represents the first such
                 application of GP for this important application.",
  notes =        "1050 hrs 15197 CEC2015",
}

Genetic Programming entries for Zheng Yin Anthony Brabazon Conall O'Sullivan Michael O'Neill

Citations