Tail Risk Assessment Using Support Vector Machine
Tail Risk Assessment Using Support Vector Machine is a scholarly work, published in 2015 in ''Journal of Engineering Science and Technology Review''. The main subjects of the publication include volatility, value at risk, econometrics, mathematics, electricity market, Markov chain, artificial intelligence, test oracle, artificial neural network, vector autoregression, support vector machine, machine learning, market forecast, and computer science. In this paper, authors apply Support Vector Regression (SVR) tool Oracle DM in forecasting volatility of Belex 15 index and estimation of Value-at-Risk (VaR).VaR is calculated using SVR model and compared to the results achieved implementing Markov Regime Switching model VaR and Feed Forward Neural Network VaR (ANN FFNN VaR).The results show that the SVR tools give better estimates of VaR comparing to other methods.