The mixed linear models are frequently used in the studies of research in medicine and social science. In this article, we presented the method of robust maximum likelihood for estimating the parameters of these models. Since this method is sensitive to outliers observations, we concentrated on...

The aim of this paper is to compare the forecasting financial distress models named the logistic mixed scoring and the semi parametric logistic scoring. Altman scoring variables are used to predict financial situation of Tunisian firms. First, the mixed logistic scoring is applied in order to...

Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterise the volatility of Bitcoin returns is widely observed in the empirical literature. In this...

In this paper we use the Bayesian Structural VAR framework to identify the major shock monetary policy shocks in Tunisia over the 1997-2015 and to provide information concerning the evolution of the economy response to these shocks. Compared with previous studies of this country, the main...

In this paper we use the Bayesian Structural VAR framework to identify the major shock monetary policy shocks in Tunisia over the 1997-2015 and to provide information concerning the evolution of the economy response to these shocks. Compared with previous studies of this country, the main...

R is a language and software that allows statistical analysis. It includes means that make it possible to manipulate data, calculations and graphical representations. It provides a wide variety of statistical tools (modeling, statistical testing, time series analysis, classification problems,...

In the last years, the financial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the financial community are implementing these models regularly. In this paper, we analyses the limitations of machine learning...

In the last years, the financial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the financial community are implementing these models regularly. In this paper, i present the different Machine Learning techniques...

This paper explores the capacity of Geometric Brownian Motion (GBM) and Prophet model to predict stock market returns during coronavirus outbreak. To the best of our knowledge this is the first research that compares GBM and prophet model in order to forcast stock market volatility especially...

The purpose of this paper is to apply count data models to predict the number of times a credit applicant will not pay the amount awarded to repay the credit. Poisson models and negative binomial distribution models, taking into account the observed heterogeneity, are generally used in...