TY - JOUR
T1 - Probabilistic Modeling of Financial Uncertainties
AU - Daneshkhah, Alireza
AU - Hosseinian-Far, Amin
AU - Chatrabgoun, Omid
AU - Sedighi, Tabassom
AU - Farsi, Maryam
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Since the global financial crash, one of the main trends in the financial engineering discipline has been to enhance the efficiency and flexibility of financial probabilistic risk assessments. Creditors could immensely benefit from such improvements in analysis hoping to minimise potential monetary losses. Analysis of real world financial scenarios require modeling of multiple uncertain quantities with a view to present more accurate, near future probabilistic predictions. Such predictions are essential for an informed decision making. In this article, the authors extend Bayesian Networks Pair-Copula Construction BN-PCC further using the minimum information vine model which results in a more flexible and efficient approach in modeling multivariate dependencies of heavy-tailed distribution and tail dependence as observed in the financial data. The authors demonstrate that the extended model based on minimum information Pair-Copula Construction PCC can approximate any non-Gaussian BN to any degree of approximation. The proposed method has been applied to the portfolio data derived from a Brazilian case study. The results show that the fitting of the multivariate distribution approximated using the proposed model has been improved compared to other previously published approaches.
AB - Since the global financial crash, one of the main trends in the financial engineering discipline has been to enhance the efficiency and flexibility of financial probabilistic risk assessments. Creditors could immensely benefit from such improvements in analysis hoping to minimise potential monetary losses. Analysis of real world financial scenarios require modeling of multiple uncertain quantities with a view to present more accurate, near future probabilistic predictions. Such predictions are essential for an informed decision making. In this article, the authors extend Bayesian Networks Pair-Copula Construction BN-PCC further using the minimum information vine model which results in a more flexible and efficient approach in modeling multivariate dependencies of heavy-tailed distribution and tail dependence as observed in the financial data. The authors demonstrate that the extended model based on minimum information Pair-Copula Construction PCC can approximate any non-Gaussian BN to any degree of approximation. The proposed method has been applied to the portfolio data derived from a Brazilian case study. The results show that the fitting of the multivariate distribution approximated using the proposed model has been improved compared to other previously published approaches.
U2 - 10.4018/ijoci.2018040101
DO - 10.4018/ijoci.2018040101
M3 - Article
SN - 1947-9344
VL - 8
SP - 1
EP - 11
JO - International Journal of Organizational and Collective Intelligence (IJOCI)
JF - International Journal of Organizational and Collective Intelligence (IJOCI)
IS - 2
ER -