A hybrid approach for predicting Value at Risk Estimation
Mohamed El Ghourabi, Amira Dridi, Fedya Telmoudi
International Journal of Information Studies, Vol. 3, No. 2, 2000.
DOI: 
Link: https://www.dline.info/ijis/fulltext/v3n2/ijisv3n2_2.pdf
Abstract:
Financial crises are perceived as shocking events, several researchers concentrated on the identification of
stressed and stable periods in order to take strategic decisions on time. In this paper, a new hybrid approach is proposed
to deal with the prediction of the Value at Risk (VaR). Based on financial variables from bankâ€™s balance sheets as input
data , this approach integrate Rough Set Theory (RST), Gaussian Case Based Reasoning- clustering (GCBR-Clustering),
Real valued Genetic Algorithm (RGA) with Support Vector Machines (SVM) in order to classify stressed and non stressed
periods and therefore determine the VaR . The RST-GCBR Clustering-RGASVM combination is justified by a high accuracy
rate which reaches 96.551% in cluster 1and 100% in cluster 2.