Unveiling the role of industries for European financial stability. Insights from the energy sector
Abstract
Extensive analysis of intertwinement with other industries caused the energy sector to gain momentum in the recent economic literature. This paper aims to create an indicator that captures the impact of financial stability for energy companies on all other industrial groups. To this end, we use daily data from 2007 until the end of 2021 to compute financial stability metrics for all European companies from the STOXX 600 index. The main contribution of our study is to harness the neural network forecasting power to predict extreme levels of this impact. We motivate this choice with evidence from the literature that documents the improved performance of these methods in predicting crises. Our methodological approach also employs an outlier detection algorithm based on copula (COPOD) to identify situations when the energy sector substantially impacts other industries and develop a framework to predict out-of-sample situations. We found evidence that the Deep Renewal model has superior forecasting accuracy to the standard Croston model. The main conclusion is that the design of this methodological framework allows authorities to monitor the impact of shocks produced by the energy sector on financial stability at the European level and undertake strategic management actions.
Keyword : financial stability, European companies, energy, COPOD, extreme levels, Deep Renewal process
How to Cite
Lupu, I., Lupu, R., & Criste, A. (2024). Unveiling the role of industries for European financial stability. Insights from the energy sector. Journal of Business Economics and Management, 25(3), 437–454. https://doi.org/10.3846/jbem.2024.21404
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Harvey, A. C., & Peters, S. (1990). Estimation procedures for structural time series models. Journal of Forecasting, 9(2), 89–108. https://doi.org/10.1002/for.3980090203
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Lautier, D., & Raynaud, F. (2012). Systemic risk in energy derivative markets: A graph-theory analysis. The Energy Journal, 33(3), 215–239. https://doi.org/10.5547/01956574.33.3.8
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Lupu, R., Călin, A. C., Zeldea, C. G., & Lupu, I. (2020). A bayesian entropy approach to sectoral systemic risk modeling. Entropy, 22(12), Article 1371. https://doi.org/10.3390/E22121371
Lupu, R., Călin, A. C., Zeldea, C. G., & Lupu, I. (2021). Systemic risk spillovers in the European energy sector. Energies, 14(19), Article 6410. https://doi.org/10.3390/EN14196410
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Nasim, A., & Downing, G. (2023). Energy shocks and bank performance in the advanced economies. Energy Economics, 118, Article 106517. https://doi.org/10.1016/J.ENECO.2023.106517
Pierret, D. (2013). The systemic risk of energy markets. SSRN. https://doi.org/10.2139/ssrn.2245811
Qian, Y., Xu, Z., Gou, X., & Škare, M. (2022). A survey on energy justice: A critical review of the literature. Economic Research-Ekonomska Istrazivanja, 36(3), Article 2155860. https://doi.org/10.1080/1331677X.2022.2155860
Restrepo, N., Uribe, J. M., & Manotas, D. (2018). Financial risk network architecture of energy firms. Applied Energy, 215, 630–642. https://doi.org/10.1016/j.apenergy.2018.02.060
Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications (4th ed.). Springer. https://doi.org/10.1007/978-3-319-52452-8
Smaga, P. (2014). The concept of systemic risk (Systemic Risk Centre Special Paper No. 5). The London School of Economics and Political Science. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477928
Tölö, E. (2020). Predicting systemic financial crises with recurrent neural networks. Journal of Financial Stability, 49, Article 100746. https://doi.org/10.1016/J.JFS.2020.100746
Tufail, M., Song, L., Umut, A., Ismailova, N., & Kuldasheva, Z. (2022). Does financial inclusion promote a green economic system? Evaluating the role of energy efficiency. Economic Research-Ekonomska Istrazivanja, 35(1), 6780–6800. https://doi.org/10.1080/1331677X.2022.2053363
Türkmen, A. C., Januschowski, T., Wang, Y., & Cemgil, A. T. (2021). Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes. PLoS ONE, 16(11), Article e0259764. https://doi.org/10.1371/JOURNAL.PONE.0259764
Yu, Y., Zhu, Y., Li, S., & Wan, D. (2014). Time series outlier detection based on sliding window prediction. Mathematical Problems in Engineering, 2014, Article 879736. https://doi.org/10.1155/2014/879736
Acharya, V. V., Pedersen, L. H., Philippon, T., & Richardson, M. (2017). Measuring systemic risk. Review of Financial Studies, 30(1), 2–47. https://doi.org/10.1093/rfs/hhw088
Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1705–1741. https://doi.org/10.1257/AER.20120555
Aggarwal, C. C. (2017). An introduction to outlier analysis. In Outlier analysis (pp. 1–34). Springer. https://doi.org/10.1007/978-3-319-47578-3_1
Ahmed, M., Naser Mahmood, A., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31. https://doi.org/10.1016/J.JNCA.2015.11.016
Algieri, B., & Leccadito, A. (2017a). Cascade effect: Measuring the contagion risk of energy companies. In Procedings of the SIE Conference 2017. https://www.siecon.org/sites/siecon.org/files/oldfiles/uploads/2017/04/Algieri.pdf
Algieri, B., & Leccadito, A. (2017b). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312–322. https://doi.org/10.1016/j.eneco.2017.01.006
Andrei, J. V., Zaharia, A., Graci, G., & Chivu, L. (2023). Energy transition or energy diversification? Assessing the complexity of energy ecosystem towards transiting a climate neutral society. Environmental Science and Pollution Research, 30(50), 108477–108511. https://doi.org/10.1007/S11356-023-30031-8/FIGURES/25
Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535–559. https://doi.org/10.1016/j.jfineco.2011.12.010
Brownlees, C., & Engle, R. F. (2017). SRISK: A conditional capital shortfall measure of systemic risk. Review of Financial Studies, 30(1), 48–79. https://doi.org/10.1093/rfs/hhw060
Butzbach, O. (2016). Systemic risk, macro-prudential regulation and organizational diversity in banking. Policy and Society, 35(3), 239–251. https://doi.org/10.1016/J.POLSOC.2016.09.002
Caccioli, F., Barucca, P., & Kobayashi, T. (2018). Network models of financial systemic risk: A review. Journal of Computational Social Science, 1(1), 81–114. https://doi.org/10.1007/s42001-017-0008-3
Chen, S., Zhong, J., & Failler, P. (2022). Does China transmit financial cycle spillover effects to the G7 countries? Economic Research-Ekonomska Istrazivanja, 35(1), 5184–5201. https://doi.org/10.1080/1331677X.2021.2025123
Croston, J. D. (2017). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289–303. https://doi.org/10.1057/JORS.1972.50
Delacre, M., Lakens, D., & Leys, C. (2017). Why psychologists should by default use welch’s t-test instead of student’s t-test. International Review of Social Psychology, 30(1), 92–101. https://doi.org/10.5334/irsp.82
Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/J.JECONOM.2014.04.012
European Central Bank. (2009). The concept of systemic risk. In Financial stability review (pp. 134–142). https://www.ecb.europa.eu/pub/pdf/fsr/financialstabilityreview200912en.pdf
Feremans, L., Vercruyssen, V., Cule, B., Meert, W., & Goethals, B. (2020). Pattern-based anomaly detection in mixed-type time series. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Lecture notes in computer science: Vol. 11906. Machine learning and knowledge discovery in databases (pp. 240–256). Springer. https://doi.org/10.1007/978-3-030-46150-8_15
Golmohammadi, K., & Zaiane, O. R. (2015, October 19–21). Time series contextual anomaly detection for detecting market manipulation in stock market. In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Paris, France. IEEE. https://doi.org/10.1109/DSAA.2015.7344856
Greenwood-Nimmo, M., Nguyen, V. H., & Rafferty, B. (2016). Risk and return spillovers among the G10 currencies. Journal of Financial Markets, 31, 43–62. https://doi.org/10.1016/j.finmar.2016.05.001
Harvey, A. C., & Peters, S. (1990). Estimation procedures for structural time series models. Journal of Forecasting, 9(2), 89–108. https://doi.org/10.1002/for.3980090203
Kerste, M., Gerritsen, M., Weda, J., & Tieben, B. (2015). Systemic risk in the energy sector – Is there need for financial regulation? Energy Policy, 78, 22–30. https://doi.org/10.1016/j.enpol.2014.12.018
Lautier, D., & Raynaud, F. (2012). Systemic risk in energy derivative markets: A graph-theory analysis. The Energy Journal, 33(3), 215–239. https://doi.org/10.5547/01956574.33.3.8
Li, Y., Liu, N., Li, J., Du, M., & Hu, X. (2019, July 14–19). Deep structured cross-modal anomaly detection. In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary. IEEE. https://doi.org/10.1109/IJCNN.2019.8852136
Li, Z., Zhao, Y., Botta, N., Ionescu, C., & Hu, X. (2020, November 17–20). COPOD: Copula-based outlier detection. In Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM) (pp. 1118–1123). Sorrento, Italy. IEEE. https://doi.org/10.1109/ICDM50108.2020.00135
Lupu, R., Călin, A. C., Zeldea, C. G., & Lupu, I. (2020). A bayesian entropy approach to sectoral systemic risk modeling. Entropy, 22(12), Article 1371. https://doi.org/10.3390/E22121371
Lupu, R., Călin, A. C., Zeldea, C. G., & Lupu, I. (2021). Systemic risk spillovers in the European energy sector. Energies, 14(19), Article 6410. https://doi.org/10.3390/EN14196410
MSCI. (n.d.). The Global Industry Classification Standard (GICS®). https://www.msci.com/our-solutions/indexes/gics
Nasim, A., & Downing, G. (2023). Energy shocks and bank performance in the advanced economies. Energy Economics, 118, Article 106517. https://doi.org/10.1016/J.ENECO.2023.106517
Pierret, D. (2013). The systemic risk of energy markets. SSRN. https://doi.org/10.2139/ssrn.2245811
Qian, Y., Xu, Z., Gou, X., & Škare, M. (2022). A survey on energy justice: A critical review of the literature. Economic Research-Ekonomska Istrazivanja, 36(3), Article 2155860. https://doi.org/10.1080/1331677X.2022.2155860
Restrepo, N., Uribe, J. M., & Manotas, D. (2018). Financial risk network architecture of energy firms. Applied Energy, 215, 630–642. https://doi.org/10.1016/j.apenergy.2018.02.060
Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications (4th ed.). Springer. https://doi.org/10.1007/978-3-319-52452-8
Smaga, P. (2014). The concept of systemic risk (Systemic Risk Centre Special Paper No. 5). The London School of Economics and Political Science. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477928
Tölö, E. (2020). Predicting systemic financial crises with recurrent neural networks. Journal of Financial Stability, 49, Article 100746. https://doi.org/10.1016/J.JFS.2020.100746
Tufail, M., Song, L., Umut, A., Ismailova, N., & Kuldasheva, Z. (2022). Does financial inclusion promote a green economic system? Evaluating the role of energy efficiency. Economic Research-Ekonomska Istrazivanja, 35(1), 6780–6800. https://doi.org/10.1080/1331677X.2022.2053363
Türkmen, A. C., Januschowski, T., Wang, Y., & Cemgil, A. T. (2021). Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes. PLoS ONE, 16(11), Article e0259764. https://doi.org/10.1371/JOURNAL.PONE.0259764
Yu, Y., Zhu, Y., Li, S., & Wan, D. (2014). Time series outlier detection based on sliding window prediction. Mathematical Problems in Engineering, 2014, Article 879736. https://doi.org/10.1155/2014/879736