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An analysis of power law distributions and tipping points during the global financial crisis

Published online by Cambridge University Press:  26 February 2018

Yifei Li ,
Lei Shi ,
Neil Allan  and
John Evans
Yifei Li
Affiliation:
Sydney Business School, University of Wollongong, Macquarie Pl, Sydney, NSW 2000, Australia
Lei Shi
Affiliation:
Sydney Business School, University of Wollongong, Macquarie Pl, Sydney, NSW 2000, Australia
Neil Allan
Affiliation:
Systems Centre, Bristol University, 4 Bridge Yard, Bradford on Avon, Wiltshire, BA15 1EJ, UK
John Evans*
Affiliation:
Centre for Analysis of Complex Financial Systems, PO Box 363, Summer Hill, NSW 2130, Australia
*
*Correspondence to: John Evans, Centre for Analysis of Complex Financial Systems, PO Box 363, Summer Hill, NSW 2130, Australia. Tel: +61414643658; E-mail: jevans@pgeaus.onmicrosoft.com
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Abstract

Heavy-tailed distributions have been observed for various financial risks and papers have observed that these heavy-tailed distributions are power law distributions. The breakdown of a power law distribution is also seen as an indicator of a tipping point being reached and a system then moves from stability through instability to a new equilibrium. In this paper, we analyse the distribution of operational risk losses in US banks, credit defaults in US corporates and market risk events in the US during the global financial crisis (GFC). We conclude that market risk and credit risk do not follow a power law distribution, and even though operational risk follows a power law distribution, there is a better distribution fit for operational risk. We also conclude that whilst there is evidence that credit defaults and market risks did reach a tipping point, operational risk losses did not. We conclude that the government intervention in the banking system during the GFC was a possible cause of banks avoiding a tipping point.

Keywords

Global financial crisisPower law distributionTipping pointUS financial risks

JEL classification

G15: International Financial Markets G18: Government Policy and Regulation
Type
Paper
Information
Annals of Actuarial Science , Volume 13 , Issue 1 , March 2019 , pp. 80 - 91
Copyright
© Institute and Faculty of Actuaries 2018 

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References

Bak, P., Tang, C. & Weisenfeld, K. (1988). Self organised criticality. Physical Review A, 38(1), 364–374.Google Scholar
Clauset, A., Shalizi, C.R. & Newman, L.E.J. (2009). Power law distributions in empirical data. SIAM Review, 51(4), 661–703.Google Scholar
Dakos, V., Scheffer, M., van Nes, E., Brovkin, V., Petoukhov, V. & Held, H. (2008). Slowing down as an early warning signal for abrupt climate change. Proceedings of the National Academy of Sciences of the United States of America, 105(38), 14308–14312.Google Scholar
Dakos, V., Scheffer, M., van Nes, E., Brovkin, V., Petoukhov, V. & Held, H. (2009). Slowing down as an early warning signal for abrupt climate change. Proceedings of the National Academy of Sciences of the United States of America, 105(36), 14308–14312.Google Scholar
de Pinho, F. & dos Santos, T. (2013). Volatility of the European stock market indices during the global financial crisis – a new proposal of stochastic volatility. Journal of Statistical and Econometric Methods, 2(2), 107–126.Google Scholar
Evans, J., Womersley, R., Wong, D. & Woodbury, G. (2008). Operational risks in banks. The Finsia Journal of Applied Finance, 2008(2), 9–116.Google Scholar
Fernholz, R.T. (2017). Nonparametric methods and local-time-based estimation for dynamic power law distributions. Journal of Applied Economics, 32, 1244–1260.Google Scholar
Gabaix, X. (2016). Power laws in economics: an introduction. Journal of Economic Perspectives, 30(1), 185–206.Google Scholar
Ganegoda, A. & Evans, J. (2012). A scaling model for severity of operational losses using generalized additive models for location scale and shape (GAMLSS). Annals of Actuarial Science, 7, 61–100.Google Scholar
Gatfaoui, H., Nagot, I. & Peretti, P.D. (2017). Are critical slowing down indicators useful to detect financial crises? In M. Billio, L. Pelizzon & R. Savona (Eds.), Systemic Risk Tomography: Signals, Measurement and Transmission Channels (pp. 73–94). Iste Press – Elsevier, London and Oxford.Google Scholar
Guttal, V., Raghavendra, S., Goel, N. & Hoarau, Q. (2016). Financial meltdowns are not critical transitions, yet rising variability could signal systemic risk. PLoS ONE, 11(1), e0144198.Google Scholar
Haldane, A. & May, R. (2011). Systemic risk in banking ecosystems. Nature, 469, 351–355.Google Scholar
Holling, C.S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematic, 4, 1–23.Google Scholar
Hommes, C. & Wagener, F. (2009). Complex evolutionary systems in behavioral finance. In T. Hens & K.R. Schenk-Hoppé (Eds.), The Handbook of Financial Markets: Dynamics and Evolution (pp. 217–265). Elsevier, Inc, North-Holland.Google Scholar
Kleinen, T., Held, H. & Petschel-Held, G. (2003). The potential role of spectral properties in detecting thresholds in the earth system: application to the thermohaline circulation. Ocean Dynamics, 53(2), 53–63.Google Scholar
Li, Y., Allan, N. & Evans, J. (2017 a). A nonlinear analysis of operational risks in Australian banks. Journal of Operational Risk, 12(1), 1–22.Google Scholar
Li, Y., Allan, N. & Evans, J. (2017 b). An analysis of operational risk events in US and European Banks 2008–2014. Annals of Actuarial Science, 11(2), 315–342.Google Scholar
Lucas, A., Klaassen, P., Spreij, P. & Straetmans, P. (2001). An analytical approach to credit risk of large corporate bond and loan portfolios. Journal of Banking and Finance, 25, 1635–1664.Google Scholar
Lui, R., Chen, P., Aihara, K. & Chen, L. (2015). Identifying early warning signals of critical transitions with strong noise by dynamic network markers. Nature Scientific Reports, 5, 17501.Google Scholar
Merton, Robert C., Billio, M., Getmansky, M., Gray, D., Lo, A.W. & Pelizzon, L. (2013). On a new approach for analyzing and managing macrofinancial risks (corrected October 2013). Financial Analysts Journal, 69(2), 22–33.Google Scholar
Mitleton-Kelly, E. (2003). Ten Principles of Complexity & Enabling Infrastructures. Elsevier, IMD, Lausanne, Switzerland.Google Scholar
Scheffer, M. (2001). Catastrophic shifts in ecosystems. Nature, 413, 591–596.Google Scholar
Scheffer, M., Bascompte, J., Brock, W., Brovkin, V., Capenter, S., Dakos, V., Held, H., Nes, E., Rietkerk, M. & Sugihara, G. (2009). Early-warning signals for critical transitions. Nature, 461, 53–59.Google Scholar
Stumpf, M. & Porter, M. (2012). Critical truths about power laws. Science, 335, 665–666.Google Scholar
Takayasu, M., Watanabe, T. & Takayasu, H. (2010). Econophysics Approaches to Large Scale Business Data and Financial Crisis. Proceedings of the Tokyo Tech-Hitotsubashi Interdisciplinary Conferencez+APFA7. Springer, Tokyo/Dordrecht/Heidelberg/London/New York.Google Scholar