Statistics data are the result of a complex scientific research process. Based on rigorous procedures, statistical research offers a bigger quantity of data from various domains, to specialists and wide public. This considerable quantity of statistics data illustrate on the one hand an extension...

The estimation of the volatility with high-frequency data is plagued by the presence of microstructure noise, which leads to biased measures. Alternative estimators have been developed and tested either on specific structures of the noise or by the speed of convergence to their asymptotic...

Coordinating contracts have been extensively researched in supply chain management. In this retrospect, we systematically review the profit allocation, decision sequence, and compliance aspects of these contracts. In addition to the existing concepts in the literature, we propose the notion of...

Prior to the financial crisis mortgage securitization models increased in sophistication as did products built to insure against losses. Layers of complexity formed upon a foundation that could not support it and as the foundation crumbled the housing market followed. That foundation was the...

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that...

Humans play important roles in the process of quantifying uncertainty. The participation of humans in this important exercise opens the process to behavioral biases. In this paper, we examine the different types of biases that may occur when quantifying uncertainty using a process-oriented...

Financial institutes have to be in a position to describe and to analyze the networks of obligors in their credit portfolios. If one obligor defaults who is numerously connected with other obligors in the portfolio there can be effects of credit contagion. We suggest a graph-based modeling of...

We examine the problem of approximating, in the Frobenius-norm sense, a positive, semidefinite symmetric matrix by a rank-one matrix, with an upper bound on the cardinality of its eigenvector. The problem arises in the decomposition of a covariance matrix into sparse factors, and has wide...

This paper improves on the issues of extreme data points and heterogeneity found in the linear programming data envelopment analysis (DEA) by presenting a cluster-adjusted DEA model (DEA with cluster approach). This analysis, based on efficiency, determines the number of clusters via Gap...