Structural Estimation of Time-Varying Spillovers: An Application to International Credit Risk Transmission

We propose a novel approach to quantify spillovers on financial markets based on a structural version of the Diebold-Yilmaz framework. Key to our approach is a SVAR-GARCH model that is statistically identified by heteroskedasticity, economically identified by maximum shock contribution and that allows for time-varying forecast error variance decompositions. We analyze credit risk spillovers between EZ sovereign and bank CDS. Methodologically, we find the model to better match economic narratives compared with common spillover approaches and to be more reactive than models relying on rolling window estimations. We find, on average, spillovers to explain 37% of the variation in our sample, amid a strong variation of the latter over time.[

Assessing spillovers between financial assets is a difficult exercise. When a shock occurs in one market and then spreads to others, prices of those markets are affected in a quasi-contemporary manner. It is difficult then, ex post, to identify the source of the shock and thus to distinguish correlation from causality in the movement of financial time series.