Modeling Risk 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) measure 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 the most accurate VaR forecasts. Especially for the high confidence levels, a risk manager must employ different volatility techniques in order to forecast accurately the VaR for the two trading positions. Different models achieve accurate VaR forecasts for long and short trading positions, indicating to portfolio managers the significance of modeling separately the left and the right side of the distribution of returns. The behavior of the risk management techniques is examined both for long and short VaR trading positions, while to best of our knowledge, this is the first study that investigates the risk characteristics of three different financial markets simultaneously. Moreover, we implement a two-stage model selection in contrast of the most commonly used backtesting procedures in the attempt to identify a unique model. Finaly, we employ parametric, non-parametric and semi-parametric techniques in order to investigate their performance in a unify environment.