GP-based rebalancing triggers for the CPPI

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

@InProceedings{Maringer:2011:CIFEr,
  author =       "Dietmar Maringer and Tikesh Ramtohul",
  title =        "GP-based rebalancing triggers for the CPPI",
  booktitle =    "IEEE Symposium on Computational Intelligence for
                 Financial Engineering and Economics (CIFEr 2011)",
  year =         "2011",
  month =        "11-15 " # apr,
  address =      "Paris",
  size =         "8 pages",
  abstract =     "The Constant Proportion Portfolio Insurance (CPPI)
                 technique is a dynamic capital-protection strategy that
                 aims at providing investors with a guaranteed minimum
                 level of wealth at the end of a specified time horizon.
                 A pertinent concern of issuers of CPPI products is when
                 to perform portfolio readjustments. One way of
                 achieving this is through the use of rebalancing
                 triggers; this constitutes the main focus of this
                 paper. We propose a genetic programming (GP) approach
                 to evolve trigger-based rebalancing strategies that
                 rely on some tolerance bounds around the CPPI
                 multiplier, as well as on the time-dependent implied
                 multiplier, to determine the timing sequence of the
                 portfolio readjustments. We carry out experiments using
                 GARCH datasets, and use two different types of fitness
                 functions, namely variants of Tracking Error and
                 Sortino ratio, for multiple scenarios involving
                 different data and/or CPPI settings. We find that the
                 GP-CPPI strategies yield better results than
                 calendar-based rebalancing strategies in general, both
                 in terms of expected returns and shortfall probability,
                 despite the fitness measures having no special
                 functionality that explicitly penalises floor
                 violations. Since the results support the viability and
                 feasibility of the proposed approach, potential
                 extensions and ameliorations of the GP framework are
                 also discussed.",
  keywords =     "genetic algorithms, genetic programming, CPPI
                 multiplier, GARCH datasets, GP-CPPI strategies,
                 GP-based rebalancing triggers, Sortino ratio, constant
                 proportion portfolio insurance technique, dynamic
                 capital-protection strategy, expected returns,
                 portfolio readjustments, shortfall probability,
                 time-dependent implied multiplier, tracking error,
                 trigger-based rebalancing strategies, autoregressive
                 processes, insurance, investment, probability",
  DOI =          "doi:10.1109/CIFER.2011.5953561",
  ISSN =         "pending",
  notes =        "Also known as \cite{5953561}",
}

Genetic Programming entries for Dietmar G Maringer Tikesh Ramtohul

Citations