Literature suggests two main approaches, parametric and non-parametric, for constructing efficiency frontiers using which efficiency scores of other units can be based. Parametric functions can be either deterministic or stochastic in nature. However, when multiple inputs and outputs are encountered, Data Envelopment Analysis (DEA), a non-parametric approach, is a powerful tool used for decades...

We derive a nonparametric test for constant beta over a fixed time interval from high-frequency observations of a bivariate Itô semimartingale. Beta is defined as the ratio of the spot continuous covariation between an asset and a risk factor and the spot continuous variation of the latter. The test is based on the asymptotic behavior of the covariation between the risk factor and an estimate o...

In this study, an Artificial Neural Network (ANN) approach is utilized to perform a parametric study on the process of extraction of lubricants from heavy petroleum cuts. To train the model, we used field data collected from an industrial plant. Operational conditions of feed and solvent flow rate, Temperature of streams and mixing rate were considered as the input to the model, whereas the flo...

The concept of Value-at-Risk is described. We discuss how this risk characteristic can be used for supervision and for internal control. Several parametric and non-parametric methods to measure Value-at-Risk are discussed. The non-parametric approach is represented by historical simulations and Monte-Carlo methods. Variance covariance and some analytical models are used to demonstrate the param...

In this paper, trends in streamflow and precipitation at the annual, seasonal and monthly timescales for different periods of records are analysed for the Aconcagua River Basin in central Chile. In this mountainous basin in the dry Andes water resources originate mainly from glaciers and seasonal snowcovers. The Mann-Kendall nonparametric test is used, and statistically significant trends are i...