Portfolio Risk Forecasting

This paper focuses on portfolio risk forecasting in an asymmetrical framework. Risk is defined by two factors; the dependence structure and the volatility. In order to account for asymmetric dependencies, the return series' interdependence is estimated via a Copula approach rather than the correlation matrix. This allows to capture tightening dependence during market turmoils and loose dependence during positive markets. In order to account for heteroskedasticity, volatility levels are forecasted via an EGARCH model. The combination of both methodologies allows precise estimates of the return series' joint distribution, respectively their joint risk. Statistical analyses state, this model quantifies the distribution tails very accurately, resulting precise VaR estimates. We also construct minimum-risk portfolios on basis of the volatility forecasts. The allocation procedures shift the portfolio shares according to predicted volatility levels. We find very low portfolio volatility levels and small downside risks, which emphasise the accuracy of our model's volatility forecasts