How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study

•This paper replicates the Diebold and Yilmaz, DY, (2012) study on financial markets connectedness.•The markets are the commodity and the stock, bond, FX for the US.•Similar to DY, we use use the Generalized Forecast Error Variance Decomposition, GEFVD.•We compare normalization schemes to GEVD.•We show that a scalar-based normalization is preferable to the row normalization suggested by DY. This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is preferable to the row normalization suggested by Diebold and Yilmaz since it yields net spillovers free of sign and ranking errors.