Header

UZH-Logo

Maintenance Infos

Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages


Mazzarisi, Piero; Zaoli, Silvia; Campajola, Carlo; Lillo, Fabrizio (2020). Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages. Journal of Economic Dynamics and Control, 121:104022.

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.

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.

Statistics

Citations

Altmetrics

Downloads

2 downloads since deposited on 11 Dec 2020
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Social Sciences & Humanities > Economics and Econometrics
Physical Sciences > Control and Optimization
Physical Sciences > Applied Mathematics
Uncontrolled Keywords:Economics and Econometrics, Control and Optimization, Applied Mathematics
Language:English
Date:1 December 2020
Deposited On:11 Dec 2020 13:18
Last Modified:28 Feb 2021 08:19
Publisher:Elsevier
ISSN:0165-1889
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.jedc.2020.104022
Project Information:
  • : FunderH2020
  • : Grant ID783206
  • : Project TitleDomino - Novel tools to evaluate ATM systems coupling under future deployment scenarios

Download

Closed Access: Download allowed only for UZH members