Autoregressive conditional tail behavior and results on Government bond yield spreads

Previous evidence in empirical finance indicates the potential usefulness of modeling time variation particularly in the tails of speculative return distributions. Based on results from extreme value theory, the present paper proposes a fixed changepoint Pareto-type autoregressive conditional tail (ARCT) model. Regression-based parameter estimation of the unobservable time-varying tail index is carried out via classical Kalman filtering. A model application highlights the tail index dynamics for daily changes in Government bond yield spreads between the U.S. Dollar and the Euro zone.