We develop a nonparametric test of productive efficiency that accounts for thepossibility of errors-in-variables. The test allows for statistical inference based on theextreme value distribution of the L?? norm. In contrast to the test proposed by Varian,H (1985): 'Nonparametric Analysis of...

The experience of past financial market turmoil suggests that in addition to eroding investor wealth, the severe consequences of rare extreme market events can spillover and impair the broader real economies. In this context, this paper is an evaluation of the methodological and empirical...

This paper intends to analyse the default risk in micro, small and medium enterprises (MSMEs) and its relation to new debt opportunities, debt overhang theory and growth intention. The results confirm that cash flow, capacity and leverage are the major determinants of firms' default, while gross...

The London Interbank Offered Rate (Libor) is a set of vital benchmark interest rates to which hundreds of trillions of dollars of financial contracts are tied. The rates are set each day via a survey of large banks. In recent years, strange behavior of the rates have caused observers to question...

Based on the seminal paper of Farrell (1957), researchers have developed several methods for measuring e fficiency. Nowadays, the most prominent representatives are nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA), both introduced in the late 1970s....

A rich literature on the analysis of efficiency in production has developed since pioneering work of Tjalling Koopmans and George Debreu in the 1950s. This literature includes work by researchers in economics, econometrics, management science, operations research, mathematical statistics and...

The objective of this paper is to provide a practical tool for stock price evaluation and forecasting under Extreme Value Theory (EVT). Three existing models are reviewed; these models include: Mordern Portfolio Theory, Black-Scholes, and Jarrow-Rudd models. It was found that these models may not...

We consider extreme value analysis in a semi-supervised setting, where we observe, next to the n data on the target variable, n +m data on one or more covariates. This is called the semi-supervised model with n labeled and m unlabeled data. By exploiting the tail dependence between the target...

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm...