Abstract
This work is intended to assess the contribution to systemic risk of major companies in the European stock market on a geographical basis. We use the EuroStoxx 50 Index as a proxy for the financial system and we rely on the CoVaR and \(\varDelta\)-CoVaR risk measures to estimate the contribution of each European country belonging to the index to systemic risk. We also conduct the significance and dominance test to evaluate whether the systemic relevance of considered countries is statistically significant and to determine which nation exerts the greatest influence on the spreading of negative spillover effects on the entire economy. Our empirical results show that, for the period ranging from 2008 to 2017, all countries contribute significantly to systemic risk, especially in times of crisis and high volatility in the markets. Moreover, it emerges that France is the systemically riskiest country, followed by Germany, Italy, Spain and Netherlands.
Similar content being viewed by others
References
Abadie, A. (2002). Bootstrap tests for distributional treatment effects in instrumental variable models. Journal of the American Statistical Association, 97(457), 284–292.
Acharya, V. V., Pedersen, L. H., Philippon, T., & Richardson, M. (2017). Measuring systemic risk. The Review of Financial Studies, 30(1), 2–47.
Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. The American Economic Review, 106(7), 1705–1741.
Algieri, B., & Leccadito, A. (2017). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312–322.
Bansal, A. (2016). Systemic importance of insurance companies–An empirical analysis. Social Science Research Network. https://ssrn.com/abstract=2741068.
Bernal, O., Gnabo, J. Y., & Guilmin, G. (2014). Assessing the contribution of banks, insurance and other financial services to systemic risk. Journal of Banking and Finance, 47, 270–287.
Bernardi, M., Gayraud, G., Petrella, L., et al. (2015). Bayesian tail risk interdependence using quantile regression. Bayesian Analysis, 10(3), 553–603.
Bernardi, M., Durante, F., Jaworski, P., Petrella, L., & Salvadori, G. (2018). Conditional risk based on multivariate hazard scenarios. Stochastic Environmental Research and Risk Assessment, 32(1), 203–211.
Bernardi, M., Maruotti, A., & Petrella, L. (2017). Multiple risk measures for multivariate dynamic heavy-tailed models. Journal of Empirical Finance, 43, 1–32.
Brownlees, CT., & Engle, RF. (2012). Volatility, correlation and tails for systemic risk measurement. Social Science Research Network. https://ssrn.com/abstract=1611229.
Castro, C., & Ferrari, S. (2014). Measuring and testing for the systemically important financial institutions. Journal of Empirical Finance, 25, 1–14.
Chao, S. K., Härdle, W. K., & Wang, W. (2015). Quantile regression in risk calibration. In Handbook of financial econometrics and statistics, Berlin: Springer (pp 1467–1489).
Gauthier, C., Lehar, A., & Souissi, M. (2012). Macroprudential capital requirements and systemic risk. Journal of Financial Intermediation, 21(4), 594–618.
Girardi, G., & Ergün, A. T. (2013). Systemic risk measurement: Multivariate GARCH estimation of CoVaR. Journal of Banking & Finance, 37(8), 3169–3180.
Koenker, R. (2005). Quantile Regression. Cambridge: Cambridge University Press.
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 46(1), 33–50.
Liu, B. Y., Ji, Q., & Fan, Y. (2017). Dynamic return-volatility dependence and risk measure of covar in the oil market: A time-varying mixed copula model. Energy Economics, 68, 53–65.
López-Espinosa, G., Moreno, A., Rubia, A., & Valderrama, L. (2012). Short-term wholesale funding and systemic risk: A global CoVaR approach. Journal of Banking and Finance, 36(12), 3150–3162.
Lum, K., Gelfand, A. E., et al. (2012). Spatial quantile multiple regression using the asymmetric laplace process. Bayesian Analysis, 7(2), 235–258.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Petrella, L., Laporta, A.G. & Merlo, L. Cross-Country Assessment of Systemic Risk in the European Stock Market: Evidence from a CoVaR Analysis. Soc Indic Res 146, 169–186 (2019). https://doi.org/10.1007/s11205-018-1881-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-018-1881-8