In many applications such as credit risk management, data are represented as high-dimensional feature vectors. It makes the feature selection necessary to reduce the computational complexity, improve the generalization ability and the interpretability. In this paper, we present a novel feature...

No abstract received.

No abstract received.

Following the Basel II Accord, with the increased focus on operational risk as an aspect distinct from credit and market risk, quantification of operational risk has been a major challenge for banks. This paper analyzes implications of the advanced measurement approach to estimate the...

Oil economies in the Former Soviet Union (FSU) region, with geographical proximity to each other, are usually impacted by some common risk factors, which make their country risks closely correlated. This paper focuses on correlation between country risks and investigates the spillovers of...

Country risk not only reflects the overall risk of a given economy but also offers other more comprehensive risk information for stakeholders' decision-making. Different from previous studies focusing on oil import portfolio risk, this paper expands the scope of risk exposure from the micro risk...

Recently, the number of consultative documents and research papers that discuss risk integration has grown considerably. This paper presents a comprehensive review of the work done on risk integration in the banking industry. This survey includes: (1) risk integration methods within regulatory...

Sovereign Wealth Funds (SWFs) are state-owned investment funds that invest in real and financial assets. Since the global financial crisis in 2008, SWFs' investments have resulted in national security concerns of host countries because SWFs continue to expand rapidly and have become increasingly...

We consider the offline scheduling problem to minimize the makespan on m parallel and identical machines with certain features. Each job and machine are labeled with the grade of service (GoS) levels, and each job can only be executed on the machine whose GoS level is no more than that of the...

In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid...