Investigating the Challenges of Data, Pricing and Modelling to Enable Agent Based Simulation of the Credit Default Swap Market

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Laleh Zangeneh",
  title =        "Investigating the Challenges of Data, Pricing and
                 Modelling to Enable Agent Based Simulation of the
                 Credit Default Swap Market",
  school =       "Computer Science, University College London",
  year =         "2014",
  address =      "UK",
  month =        jul # " 20",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, Gaussian Process Regression",
  URL =          "",
  URL =          "",
  size =         "174 pages",
  abstract =     "The Global Financial Crisis of 2007-2008 is considered
                 by three top economists the worst financial crisis
                 since the Great Depression of the 1930s [Pendery,
                 2009]. The crisis played a major role in the failure of
                 key businesses, declines in consumer wealth, and
                 significant downturn in economic activities leading to
                 the 2008-2012 global recession and contributing to the
                 European sovereign-debt crisis [Baily and Elliott,
                 2009] [Williams, 2012]. More importantly, the serious
                 limitation of existing conventional tools and models as
                 well as a vital need for developing complementary tools
                 to improve the robustness of existing overall framework
                 immediately became apparent. This thesis details three
                 proposed solutions drawn from three main subject areas:
                 Statistic, Genetic Programming (GP), and Agent-Based
                 Modelling (ABM) to help enable agent-based simulation
                 of Credit Default Swap (CDS) market. This is
                 accomplished by tackling three challenges of lack of
                 sufficient data to support research, lack of efficient
                 CDS pricing technique to be integrated into agent based
                 model, and lack of practical CDS market experimental
                 model, that are faced by designers of CDS investigation
                 tools. In particular, a general data generative model
                 is presented for simulating financial data, a novel
                 price calculator is proposed for pricing CDS contracts,
                 and a unique CDS agent-based model is designed to
                 enable the investigation of market. The solutions
                 presented can be seen as modular building blocks that
                 can be applied to a variety of applications.
                 Ultimately, a unified general framework is presented
                 for integrating these three solutions. The motivation
                 for the methods is to suggest viable tools that address
                 these challenges and thus enable the future realistic
                 simulation of the CDS market using the limited real
                 data in hand. A series of experiments were carried out,
                 and a comparative evaluation and discussion is
                 provided. In particular, we presented the advantages of
                 realistic artificial data to enable open ended
                 simulation and to design various scenarios, the
                 effectiveness of Cartesian Genetic Programming (CGP) as
                 a bio-inspired evolutionary method for a complex
                 real-world financial problem, and capability of Agent
                 Based (AB) models for investigating CDS market. These
                 experiments demonstrate the efficiency and viability of
                 the proposed approaches and highlight interesting
                 directions of future research.",
  notes =        "Supervisor Peter J. Bentley",

Genetic Programming entries for Laleh Zangeneh