Detecting Giver and Receiver Spillover Groups in Large Vector Autoregressions

I propose an algorithm that partitions the series of a large vector autoregression (VAR) into groups based on the spillover structure. The novelty of the procedure is that it is capable of simultaneously detecting both the giver and receiver group structures. I study the properties of the algorithm when the data are generated by a class of network-based VAR models and show that it consistently detects the groups within this class. The methodology is applied to study the spillover group structure in a panel of volatility measures for the constituents of the S&P 100.