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Approximating non-gaussian bayesian networks using minimum information vine model with applications in financial modelling
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Chatrabgoun, O., Hosseinian-Far, A., Chang, V., Stocks, Nigel G. and Daneshkhah, Alireza (2018) Approximating non-gaussian bayesian networks using minimum information vine model with applications in financial modelling. Journal of Computational Science, 24 . pp. 266-276. doi:10.1016/j.jocs.2017.09.002 ISSN 1877-7503 .
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Official URL: https://doi.org/10.1016/j.jocs.2017.09.002
Abstract
Many financial modeling applications require to jointly model multiple uncertain quantities to presentmore accurate, near future probabilistic predictions. Informed decision making would certainly benefitfrom such predictions. Bayesian networks (BNs) and copulas are widely used for modeling numerousuncertain scenarios. Copulas, in particular, have attracted more interest due to their nice property ofapproximating the probability distribution of the data with heavy tail. Heavy tail data is frequentlyobserved in financial applications. The standard multivariate copula suffer from serious limitations whichmade them unsuitable for modeling the financial data. An alternative copula model called the pair-copulaconstruction (PCC) model is more flexible and efficient for modeling the complex dependence of finan-cial data. The only restriction of PCC model is the challenge of selecting the best model structure. Thisissue 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 learnedfrom 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 furtherusing the minimum information vine model which results in a more flexible and efficient approach inunderstanding the complex dependence between multiple variables with heavy tail dependence andasymmetric features which appear widely in the financial applications.
| Item Type: | Journal Article | ||||||||
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| Subjects: | H Social Sciences > HA Statistics H Social Sciences > HG Finance Q Science > QA Mathematics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Journal or Publication Title: | Journal of Computational Science | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 1877-7503 | ||||||||
| Official Date: | January 2018 | ||||||||
| Dates: |
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| Volume: | 24 | ||||||||
| Page Range: | pp. 266-276 | ||||||||
| DOI: | 10.1016/j.jocs.2017.09.002 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 27 October 2017 | ||||||||
| Date of first compliant Open Access: | 6 September 2018 |
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