A1142
Title: (Quantile) spillover indexes: Simulation-based evidence, confidence intervals and a decomposition
Authors: Giovanni Bonaccolto - University of Enna Kore (Italy) [presenting]
Massimiliano Caporin - University of Padova (Italy)
Jawad Shahzad - Montpellier Business School (France)
Abstract: Quantile-spillover indexes have recently become popular for analyzing tail interdependence. It is shown that the estimation of spillover indexes is affected by a positive distortion when the parameters of the underlying fitted models are not evaluated with respect to their statistical significance or are not estimated subject to regularization. The distortion is reduced for increasing sample sizes, thanks to the consistency of estimators, or by filtering out non-significant parameters, even if in small samples, it does not fully disappear due to type I error. In the next step, a simulation-based approach is introduced to recovering confidence intervals from quantile spillover indexes. In addition, an algebraic decomposition of quantile spillover is put forward, separating the dynamic interdependence from the contemporaneous interdependence (due to residual correlation). Empirical evidence on equity sector indices shows that distortions on real data are sizable, and the decomposition points out that most of the spillover is due to contemporaneous effects. All of the results extend and are confirmed for the Spillover index.
