Portfolio Value-at-Risk Quantitative Analysis and Back-testing using programming techniques

Portfolio Value-at-Risk Quantitative Analysis and Back-testing using programming techniques

2010

Tai Tran

Abstract

Risk management is an important aspect of portfolio management, especially in turbulent times. This document discusses the risk management technique using Value-at-Risk (VaR) and the role of back-testing in validating the VaR calculation models. By using historical data, VaR and portfolio gain/loss is computed using programming language Ox and graphs are created. Three VaR calculation methods are used, one being the Normality Variance-Covariance approach, the second Historical Simulation and the other Monte Carlo stochastic Simulation. Overall, I find all models successfully meet the Basel II regulatory requirement. Simulation, while guarding the portfolio a higher VaR value, comes at a locked-in capital reserve.

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