Dynamic implied correlation modeling and forecasting in structured finance

Abstract

Correlations are the main drivers for credit portfolio risk and constitute a major element in pricing credit derivatives such as synthetic single-tranche collateralized debt obligation swaps. This study suggests a dynamic panel regression approach to model and forecast implied correlations. Random effects are introduced to account for unobservable time-specific effects on implied tranche correlations. The implied-correlation forecasts of tranche spreads are compared to forecasts using historical correlations from asset returns. The empirical findings support our proposed dynamic mixed-effects regression correlation model. © 2013 Wiley Periodicals, Inc.