Probabilistic Modeling of Financial Uncertainties

Abstract

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 paper, we 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. We demonstrate that the extended model based on minimum information PairCopula Construction (PCC) can approximate any non-Gaussian BN to any degree of approximation. Our 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.

A Super Simple Life-cycle Cost Estimation Model with Minimum Data Requirement

“…The advantages and capabilities of ABC have been discussed in the literature by [19][20][21][22][23][24][25]. In addition to the importance of life-cycle cost estimation methodologies, the significance of uncertainty analysis on cost estimations and its influence on robustness and reliability of the cost related decisions are highlighted in [26,27].…”