Tail Granger Causalities and Where to Find Them: Extreme Risk Spillovers vs Spurious Linkages

22 Pages Posted: 1 Jun 2020

See all articles by Piero Mazzarisi

Piero Mazzarisi

Scuola Normale Superiore

Silvia Zaoli

Abdus Salam International Centre for Theoretical Physics (ICTP)

Carlo Campajola

Scuola Normale Superiore

Fabrizio Lillo

Università di Bologna

Date Written: May 3, 2020

Abstract

Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether past extreme events of a time series help predicting future extreme events of another time series. The topology and connectedness of networks built with Granger causality in tail can be used to measure systemic risk and to identify risk transmitters. Here we introduce a novel test of Granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics. The proposed test has very good size and power in finite samples, especially for large sample size, allows inferring the correct time scale at which the causal interaction takes place, and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables. An extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of Granger causality in tail by [Hong et al., 2009]. We report both advantages and drawbacks of the different approaches, pointing out some crucial aspects related to the false detections of Granger causality for tail events. An empirical application to high frequency data of a portfolio of US stocks highlights the merits of our novel approach.

Keywords: Granger causality in risk, Spillover effects, Financial contagion, Autoregressive processes, Likelihood-Ratio test

JEL Classification: C12, C30, G10

Suggested Citation

Mazzarisi, Piero and Zaoli, Silvia and Campajola, Carlo and Lillo, Fabrizio, Tail Granger Causalities and Where to Find Them: Extreme Risk Spillovers vs Spurious Linkages (May 3, 2020). Available at SSRN: https://ssrn.com/abstract=3591958 or http://dx.doi.org/10.2139/ssrn.3591958

Piero Mazzarisi (Contact Author)

Scuola Normale Superiore ( email )

Piazza dei Cavalieri, 7
Pisa, 56126
Italy

Silvia Zaoli

Abdus Salam International Centre for Theoretical Physics (ICTP) ( email )

Strada Costiera 11
Trieste, 34151
Italy

Carlo Campajola

Scuola Normale Superiore ( email )

Piazza dei Cavalieri, 7
Pisa, 56126
Italy

Fabrizio Lillo

Università di Bologna ( email )

Via Zamboni, 33
Bologna, 40126
Italy

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