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Approximating Non-Gaussian Bayesian Networks using Minimum Information Vine Model with Applications in Financial Modelling

Chatrabgoun, Omid; Hosseinian-Far, Amin; Chang, Victor; Stocks, Nigel G. and Daneshkhah, Alireza (2017). Approximating Non-Gaussian Bayesian Networks using Minimum Information Vine Model with Applications in Financial Modelling. Journal of Computational Science (Early Access).

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DOI (Digital Object Identifier) Link: https://doi.org/10.1016/j.jocs.2017.09.002
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Abstract

Many financial modeling applications require to jointly model multiple uncertain quantities to present more accurate, near future probabilistic predictions. Informed decision making would certainly benefit from such predictions. Bayesian Networks (BNs) and copulas are widely used for modeling numerous uncertain scenarios. Copulas, in particular, have attracted more interest due to their nice property of approximating the probability distribution of the data with heavy tail. Heavy tail data is frequently observed in financial applications. The standard multivariate copula suffer from serious limitations which made them unsuitable for modeling the financial data. An alternative copula model called the Pair-Copula Construction (PCC) model is more flexible and efficient for modeling the complex dependence of financial data. The only restriction of PCC model is the challenge of selecting the best model structure. This issue can be tackled by capturing conditional independence using the Bayesian Network PCC (BN-PCC). The flexible structure of this model can be derived from conditional independences statements learned from data. Additionally, the difficulty of computing conditional distributions in graphical models for non-Gaussian distributions can be eased using pair-copulas. In this paper, we extend this approach further using the minimum information vine model which results in a more flexible and efficient approach in understanding the complex dependence between multiple variables with heavy tail dependence and asymmetric features which appear widely in the financial applications.

Item Type: Journal Item
Copyright Holders: 2017 Elsevier
ISSN: 1877-7503
Project Funding Details:
Funded Project NameProject IDFunding Body
Strategic Package: Centre for Predictive Modelling in Science and EngineeringEP/L027682/1UK Engineering & Physical Sciences Research Council
Keywords: Bayesian Network; Copula; Directed Acyclic Graph; Entropy; Orthonormal Series; Probabilistic Financial Modelling; Vine
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 50847
Depositing User: Amin Hosseinian Far
Date Deposited: 07 Sep 2017 10:51
Last Modified: 07 Sep 2017 10:51
URI: http://oro.open.ac.uk/id/eprint/50847
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