On the failure and systemic risk of innovation cluster: copula approach
Abstract
In order to assess and parameterize the risk of innovation activity implemented by innovation clusters, it is necessary to determine the reliable tools of measuring of systemic risk.
Purpose – to propose an adequate approach to evaluate the systemic risk with regard to the impact of interlinkages between cluster entities and other external factors.
Research methodology – general overview of research papers and documents presenting concepts and methodologies of evaluation of systemic risk and performance of networked structures as approach to evaluate the systemic risk with regard to the impact of interlinkages between cluster entities and other external factors, applied research.
Findings – it is suggested to develop the further parameterization of intensity.
Modelling of the tail dependence and asymmetric dependence between pairs of networked positions remains an important task.
Research limitations – the lack of information concerning the structure and types of interactions and relationship between the members of innovation cluster. There are made some additional assumptions related to reduced-form approach of credit risk modelling.
Practical implications – proposed conceptual model of evaluation of systemic risk should be useful for understanding and further treatment of measuring risk in a case of innovation management.
Originality/Value – the concept of the measuring the systemic risk in innovation cluster as a joint probability of correlated failure of commercialization of innovative activity results is proposed and analysed in this paper.
Keyword : correlation, dependence structure, systemic risk, failure
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Acharya, V. V., & Yorulmazer, T. (2007). Too many to fail – An analysis of time-inconsistency in bank closure policies. Journal of Financial Intermediation, 16(1), 1–31. https://doi.org/10.1016/j.jfi.2006.06.001
Acharya, V. V., & Yorulmazer, T. (2008). Cash-in-the-market pricing and optimal resolution of bank failures. Review of Financial Studies, 21(6), 2705–2742. https://doi.org/10.1093/rfs/hhm078
Adrian, T., & Brunnermeier, M. (2011). CoVaR (NBER Working paper No. 17454). National Bureau of Economic Research. https://doi.org/10.3386/w17454
Beaudry, C., & Breschi, S. (2000). Does “clustering” really help firms’ innovative activities? Centro Studi sui Pro-cessi di Internazionalizzazione, Università Commerciale Luigi Bocconi. ftp://ftp.unibocconi.it/pub/RePEc/cri/papers/wp111.pdf
Benoit, S., Colletaz, G., Hurlin, Ch., & Pérignon, Ch. (2013). A theoretical and empirical comparison of systemic risk measures. https://halshs.archives-ouvertes.fr/halshs-00746272
Bernardi, M., & Catania, L. (2015). Switching generalized autoregressive score copula models with application to systemic risk. Journal of Applied Econometrics, 34(1), 43–65. https://doi.org/10.1002/jae.2650
Elizalde, A. (2006). Credit risk models I: default correlations in intensity models (CEMFI Working Paper No. 0605). Center for Monetary and Financial Studies.
Embrechts, P., McNeil, A., & Straumann, D. (2002). Correlation and dependency in risk management: Properties and pitfalls. In M. A. H. Dempster (Ed.), Risk management: Value at risk and beyond (pp. 176–223). Cambridge University Press. https://doi.org/10.1017/CBO9780511615337.008
Fermanian, J.-D. (2017). Recent developments in copula models. Econometrics, 5(3), 1–3. https://doi.org/10.3390/econometrics5030034
Frey, R., & Backhaus, J. (2004, September). Portfolio credit risk models with interacting default intensities: a Markovian approach (Working paper). University of Leipzig.
Gersbach, H., & Lipponer, A. (2003). Firm defaults and the correlation effects. European Financial Management, 9(3), 361–377. https://doi.org/10.1111/1468-036X.00225
Giesecke, K. (2004). Correlated default with incomplete information. Journal of Banking & Finance, 28(7), 1521–1545. https://doi.org/10.1016/S0378-4266(03)00129-8
Guzmics, S., & Pflug, G. Ch. (2019). Modeling cascading effects for systemic risk: Properties of the Freund copula. Dependence Modeling, 7(1), 24–44. https://doi.org/10.1515/demo-2019-0002
Hirshleifer, D., Subrahmanyam, A., & Titman, S. (1994). Security analysis and trading patterns when some investors receive information before others. The Journal of Finance, 49(5), 1665–1698. https://doi.org/10.1111/j.1540-6261.1994.tb04777.x
Kleinow, J., & Moreira, F. (2016). Systemic risk among European banks: A copula approach. Journal of International Financial Markets, Institutions and Money, 42, 27–42. https://doi.org/10.1016/j.intfin.2016.01.002
Kole, E., Koedijk, K., & Verbeek, M. (2005). Testing copulas to model financial dependence. Rotterdam.
Moussa, A. (2011). Contagion and systemic risk in financial networks. Columbia University. https://doi.org/10.7916/D8T159MH
Neftci, S. (2002). Correlation of default events. Some new tools (ISMA Discussion Papers in Finance 2002-17). ISMA Centre.
Nelsen, R. B. (1999). An introduction to copulas. In Lecture notes in statistics: Vol. 139 (pp. 1–4). Springer-Verlag. https://doi.org/10.1007/978-1-4757-3076-0
Ning, C. (2010). Dependence structure between the equity market and the foreign exchange market – A copula approach. Journal of International Money and Finance, 29(5), 743–759. https://doi.org/10.1016/j.jimonfin.2009.12.002
Owen-Smith, J., & Powell, W. W. (2004). Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community. Organization Science, 15(1), 5–21. https://doi.org/10.1287/orsc.1030.0054
Schönbucher, Ph. J., & Schubert, D. (2001). Copula-dependent default risk in intensity models (Working Paper, Department of Statistics). Bonn University. https://doi.org/10.2139/ssrn.301968
Schönbucher, Ph. J. (2000). Factor models for portfolio credit risk (Bonn Econ Discussion Papers). Universität Bonn. http://hdl.handle.net/10419/78427
Segoviano, P., & Goodhart, C. (2009). Banking stability measures (Financial Markets Group Working paper). London School of Economics and Political Science. https://doi.org/10.5089/9781451871517.001
Staudt, A. (2010). Tail risk, systemic risk and copulas. Casualty Actuarial Society E-Forum, 2, 1–23.
Valuzis, M., & Gudelyte, L. (2017, April). On the evaluation of synergy and systemic risk in innovation creating business cluster. In Proceedings of the 5th Business & Management Conference (pp. 178– 192). Rome. https://doi.org/10.20472/BMC.2017.005.014
Wasserman, S., & Faust, K. (1994). Social network analysis. Methods and applications. Cambridge University Press. https://lib.ugent.be/catalog/rug01:000343905
Xihong, Q., Wankli, X., & Kongyue, L. (2010). Do entrepreneurial social networks boost enterprise growth? Evidence from the Pearl River Delta in China. Frontiers of Business Research in China, 4(3), 498–513. https://doi.org/10.1007/s11782-010-0108-x