This article aims to identify the most relevant variables that allow through a neural network model (RNA), with supervised learning, in a kind of error correction and feedforward perceptron multilayer architecture to achieve the best predictors of low risk, in the process of microcredit....

This research shows the application and performance of three models for the classification of credit applicants: discriminant analysis, logistic regression and neural networks; techniques used by financial institutions for the calculation of credit scoring. The results show a better performance...

This research shows the application and performance of three models for the classification of credit applicants: discriminant analysis, logistic regression and neural networks; techniques used by financial institutions for the calculation of credit scoring. The results show a better performance...

This article aims to identify the most relevant variables that allow through a neural network model (RNA), with supervised learning, in a kind of error correction and feedforward perceptron multilayer architecture to achieve the best predictors of low risk, in the process of microcredit....

This article aims to identify the most relevant variables that allow through a neural network model (RNA), with supervised learning, in a kind of error correction and feedforward perceptron multilayer architecture to achieve the best predictors of low risk, in the process of microcredit....

Spanish Abstract: Esta investigación tiene como propósito implementar la metodología de regresión cuantil bayesiana en el cálculo del valor en riesgo (VaR, en inglés) en el mercado de valores colombiano. Para este objetivo se valoran algunos requerimientos regulatorios sobre riesgo de...

The spurious regression phenomenon, identified by Granger and Newbold (1974) is well known in econometrics. In fact, spurious regression occurs under a wide variety of Data Generating Processes: driftless unit root, unit root with drift, trend stationarity, broken-trend stationarity,… However,...

We present a new heteroskedastic conditional variance model using NonLinear Moving Average as the basis for this specification [NLMACH(q)]. The typical problem of this class of models-i.e., noninvertibility—is solved by means of an intuitive parametric restriction; this allows us to use...