A0167
Title: (Quantile) Spillover indexes: Simulation-based evidence, confidence intervals and a decomposition
Authors: Massimiliano Caporin - University of Padova (Italy) [presenting]
Giovanni Bonaccolto - University of Enna Kore (Italy)
Jawad Shahzad - Montpellier Business School (France)
Abstract: Quantile-spillover indexes have recently become popular for analysing tail interdependence. In an extensive simulation study, we show that the estimation of spillover indexes is affected by a positive distortion when the parameters of the underlying fitted models are not evaluated in terms of their statistical significance. 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. We make another step by introducing a simulation-based approach to recovering confidence intervals from quantile spillover indexes. In addition, we put forward an algebraic decomposition of quantile spillover separating the dynamic interdependence from the contemporaneous interdependence (due to residual correlation). Empirical evidence shows that distortions in real data are sizable, and the decomposition points out that most of the spillover is due to contemporaneous effects. All of our results extend and are confirmed for the Spillover index.
