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

Francesco Giuseppe Caloia; Andrea Cipollini; Silvia Muzzioli

doi:10.1016/j.eneco.2019.104536

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

Francesco Giuseppe Caloia, Andrea Cipollini^*, Silvia Muzzioli

^*Corresponding author for this work

Economics

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

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.

Original language	English
Article number	104536
Pages (from-to)	1-13
Number of pages	13
Journal	Energy Economics
Volume	84
Early online date	23 Oct 2019
DOIs	https://doi.org/10.1016/j.eneco.2019.104536
Publication status	Published - Oct 2019

Keywords

Causality
Generalized forecast error variance decomposition
Normalization schemes
Simulation
Spillover
Vector autoregression models

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

abstract = "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.",

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