Robust Bootstrap Densities for Dynamic Conditional Correlations: Implications for Portfolio Selection and Value-at-Risk

Abstract

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on the forecast of the conditional variances, covariances and correlations of financial returns. Although the decisions are based on forecasts covariance matrix little is known about effects of outliers on the uncertainty associated with these forecasts. In this paper we analyse these effects on the context of dynamic conditional correlation (DCC) models when the uncertainty is measured using bootstrap methods. We also propose a bootstrap procedure to obtain forecast densities for return, volatilities, conditional correlation and VaR that is robust to outliers. The results are illustrated with simulated and real data.