Zangeneh, L; (2014) Investigating the Challenges of Data, Pricing and Modelling to Enable Agent Based Simulation of the Credit Default Swap Market. Doctoral thesis , UCL (University College London).

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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 Modeling (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.

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