Paper Info
Title
GP-based rebalancing triggers for the CPPI
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.
Year
DOI
Venue
2011
10.1109/CIFER.2011.5953561
Computational Intelligence for Financial Engineering and Economics
Keywords
Field
DocType
autoregressive processes,genetic algorithms,insurance,investment,probability,CPPI multiplier,GARCH datasets,GP-CPPI strategies,GP-based rebalancing triggers,Sortino ratio,constant proportion portfolio insurance technique,dynamic capital-protection strategy,expected returns,genetic programming,portfolio readjustments,shortfall probability,time-dependent implied multiplier,tracking error,trigger-based rebalancing strategies
Economics,Mathematical optimization,Actuarial science,Sortino ratio,Time horizon,Genetic programming,Portfolio,Constant proportion portfolio insurance,Autoregressive conditional heteroskedasticity,Genetic algorithm,Tracking error
Conference
ISSN
ISBN
Citations 
pending
978-1-4244-9933-5
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Dietmar Maringer115611.35
Tikesh Ramtohul200.34