Modeling Risk : VaR Methods for Long and Short Trading Positions

The accuracy of parametric, non-parametric and semi-parametric methods in predicting the one-day-ahead Value-at-Risk (VaR) of perfectly diversified portfolios in three types of markets (stock exchanges, commodities and exchange rates) is investigated, both for long and short trading positions. The risk management techniques are designed to capture the main characteristics of asset returns, such as leptokurtosis and asymmetric distribution, volatility clustering, asymmetric relationship between stock returns and conditional variance and power transformation of conditional variance. Based on backtesting measures and a loss function evaluation method, we find out that the modeling of the main characteristics of asset returns produces accurate VaR forecasts. Especially for the high confidence levels, a risk manager must employ different volatility techniques in order to forecast the VaR for the two trading positions