Abstract: Credit Risk Assessment is a crucial part of decision making in any financial institutes. The aim of this paper is to highlight and illustrate the use of some quantitative techniques for risk estimation in finance and insurance. We will study the theoretical properties, the accuracy of modelling the economic phenomena and the computational performances of the risk measures Value-at-Risk, Conditional Tail Expectation, Conditional Value-at-Risk and Limited Value-at-Risk in the case of logistic distribution. We also investigate the most important statistical estimation methods for risk measure evaluation and we will compare their theoretical and empirical behavior.
Keywords: risk management, classification, data mining, market manipulation, Support Vector Machine (SVM), Stock Market, Machine Learning, Feature Selection