Estimate and evaluate non-parametric value at risk and expected shortfall based on principal component analysis in Tehran Stock Exchange

In this research, the application of Monte Carlo simulation based Principal component analysis (PCA), as a nonparametric approach for calculating value at risk and expected shortfall, has been studied. This method tries to overcome some problems of conventional Monte Carlo simulation method such as excessive and time consuming calculation,. In this order, by applying Monte Carlo simulation based PCA method, we calculate VaR and ES for Tehran Stock Exchange industrial indices and compare its results with the results obtained by riskmetrics method and conventional Monte Carlo simulation method. Results from backtesting technics show Monte Carlo simulation based PCA and conventional Monte Carlo simulation method both have the same accuracy in estimating VaR and ES, but riskmetrics could not estimate VaR and ES as well like last methods. Also, assessing time needed for estimating VaR and ES shows Monte Carlo simulation based PCA a quicker method than conventional Monte Carlo simulation.