Skip to main content

Complexity Analysis and Systemic Risk in Finance: Some Methodological Issues

  • Chapter
  • First Online:
Network Models in Economics and Finance

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 100))

  • 3062 Accesses

Abstract

The standard financial analysis has proven unable to provide an adequate understanding and therefore a timely warning of the financial crisis. In order to strengthen financial stability, policy makers are looking for new analytical tools to identify and address sources of systemic risk. Complexity theory and network analysis can make a useful contribution. The financial crisis has highlighted the need to look at the links and interconnections in the financial system. Complexity and network theory which can help identify the extent to which the financial system is resilient to contagion as well as the nature of major triggers and channels of contagion. However, the methodological suitability of the premises of complexity theory for financial systems is still debatable. The use of complexity analysis in finance draws on two distinct but related strands of theory: econophysics and econobiology. Each strand is associated with advantages and drawbacks in explaining the dynamics of financial systems. Properly combined, these theories could form a coherent body of theoretical premises that are capable of approximating reality in financial systems, i.e. explain the “stylized facts”, better than the traditional financial analysis model, which is crucially based on the false conception of a Gaussian distribution of financial returns.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adamu, K., Phelps, S.: Modeling financial time series using grammatical evolution. Working Paper (2009). http://web.mac.com/davidrh/AMLCF09/papers/5.pdf

  2. Adya, M., Collopy, F.: How effective are neural vided fine research assistance. J. Forecast. 17, 481–495 (1998)

    Article  Google Scholar 

  3. Aleksiejuk, A., Holyst, A.J., Kossinets, G.: Self-organized Criticality in a model of collective bank bankruptcies. Int. J. Mod. Phys. C 13, 333 (2002)

    Article  Google Scholar 

  4. Allen, F., Morris, S.T., Shin, H.S.: Beauty contests and iterated expectations in asset markets. Rev. Financ. Stud. 19(3), 719–752 (2006)

    Article  Google Scholar 

  5. Amaral, L.A.N., Ottino, J.M.: Complex networks: augmenting the framework for the study of complex systems. Eur. Phys. J. B 38, 147–162 (2004)

    Article  Google Scholar 

  6. Anand, K., Brennan, S., Gai, P., Kapadia, S., Willison, M.: Complexity and crises in financial systems. Paper presented at joint D-FS/DG-P workshop on recent advances in modelling systemic risk using network analysis. ECB, Frankfurt am Main, 5 October 2009

    Google Scholar 

  7. Arthur, B.: Complexity in economic and financial markets. Complexity 1(1), 20–25 (1995)

    Article  MATH  Google Scholar 

  8. Arthur, B.: Complexity and the economy. Science 284, 107–109 (1999)

    Google Scholar 

  9. Arthur, W.B., Holland, J., LeBaron, B., Palmer, R., Taylor, P.: Asset pricing under endogenous expectations in an artificial stock market. In: Arthur, W.B., Durlauf, S., Lane, D.A. (eds.) The Economy as an Evolving Complex System II, Santa Fe Studies in the Sciences of Complexity, Westview Press, pp 15–44 (1996)

    Google Scholar 

  10. Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans. Neural Netw. 12, 929–935 (2001)

    Article  Google Scholar 

  11. Axtell, R., Epstein, J.: Growing Artificial Societies from the Bottom Up. Brookings Institution Press, Washington (1996)

    Google Scholar 

  12. Azzini, A., Tettamanzi, A.: A neural evolutionary approach to financial modeling. Proc. Genet. Evol. Comput. Conf. 2, 1605–1612 (2006)

    Google Scholar 

  13. Bak, P., Paczuski, M., Shubik, M.: Price variations in a stock market with many agents. Physica A 246, 430–440 (1997)

    Article  Google Scholar 

  14. Bank of International Settlements: a review of financial market events in Autumn 1998. CGFS publication No 12 (1999)

    Google Scholar 

  15. Bartolozzi, M., Thomas, A.W.: Stochastic cellular automata model for stock market dynamics. Phys. Rev. E 69, 046112 (2004)

    Article  Google Scholar 

  16. Bech, M.L., Adelstein, I.: Payments, crunch and easing. Paper presented at Joint D-FS/DG-P workshop on recent advances in modelling systemic risk using network analysis, ECB, Frankfurt am Main, 5 October 2009

    Google Scholar 

  17. Bech, M.L., Beyeler, W., Glass, R.J., Soramäki, K.: Network topology and payment system resilience. Paper presented at Joint D-FS/DG-P workshop on recent advances in modeling systemic risk using network analysis, ECB, Frankfurt am Main, 5 October 2009

    Google Scholar 

  18. Becher, C., Millard, S., Soramäki, K.: The network topology of CHAPS Sterling. Bank of England Working Paper No 355 (2008)

    Google Scholar 

  19. Beinhocker, E.D.: The Origin of Wealth–Evolution, Complexity, and the Radical Remaking of Economics. Cambridge MA: Harvard Business School Press (2006)

    Google Scholar 

  20. Borgatti, S.: Centrality and network flow. Soc. Netw. 27, 55–71 (2005)

    Article  Google Scholar 

  21. Borland, L.: Long-range memory and non-extensivity in financial markets. Europhys. News 36, 228–231 (2005)

    Article  Google Scholar 

  22. Bouchaud, J.-F., Potters, M.: More stylized facts of financial markets: leverage effect and downside correlations. Physica A 299, 60–70 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  23. Bouchaud, J.-F., Gefen, Y., Potters, M., Wyart, M.: Fluctuations and response in financial markets: the subtle nature of ‘random’ price changes. Quant. Financ. 4, 176–190 (2004)

    Article  Google Scholar 

  24. Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modeling. Springer, Berlin (2006)

    Google Scholar 

  25. Brady, N.: Report of the Presidential Task Force on Market Mechanisms. GPO, Washington (1988)

    Google Scholar 

  26. Brock, W.A.: Scaling in economics: a reader’s guide. Ind. Corp. Chang. 8(3), 409–446 (1999)

    Article  Google Scholar 

  27. Brunnermeier, M.K., Crocket, A., Goodhart, C., Persaud, A.D., Shin, H.: The Fundamental Principles of Financial Regulation. Geneva Reports on the World Economy, 11 (2009)

    Google Scholar 

  28. Caballero, R.J., Simsek, A.: Complexity and financial panics. NBER Working Paper No 14997 (2009)

    Google Scholar 

  29. Castren, O., Kavonius, I.K.: Balance sheet contagion and systemic risk in the euro area financial system: a network approach. ECB Working Paper No 1124 (2009)

    Google Scholar 

  30. Chen, W.H., Shih, J.Y.: A study of Taiwan’s issuer credit rating systems using support vector machines. Expert Syst. Appl. 30, 427–435 (2006)

    Article  Google Scholar 

  31. Cont, R.: Empirical properties of asset returns: stylized facts and statistical issues. Quant. Financ. 1(2), 223–236 (2001)

    Article  Google Scholar 

  32. Coolen, A.C.C.: The Mathematical Theory of Minority Games: Statistical Mechanics of Interacting Agents. Oxford University Press, Oxford (2004)

    Google Scholar 

  33. Crutchfield, J.: Is anything ever new? Considering emergence. In: Cowan, G., Pines, D., Meltzer, D. (eds.) Complexity: Metaphors, Models, and Reality. Addison-Wesley, Redwood City (1994)

    Google Scholar 

  34. Delli Gatti, D., Gallegati, M., Greenwald, B., Russo, A., Stiglitz, J.E.: Business fluctuations in a credit-network economy. Phys. A Stat. Mech. Appl. 370(1), 68–74 (2006)

    Article  MathSciNet  Google Scholar 

  35. Derman, E.: Models Behaving Badly: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life. Wiley Finance, London (2011)

    Google Scholar 

  36. Durlauf, S.: Complexity and empirical economics. Econ. J. 115(504), 225–243 (2005)

    Article  Google Scholar 

  37. Durlauf, S.: Complexity, economics and public policy. Politics Philos. Econ. 11, 45–75 (2012)

    Article  Google Scholar 

  38. Epstein, J.: Generative Social Science. Princeton University Press, Princeton (2006)

    MATH  Google Scholar 

  39. Espinosa-Vega, M.A., Sole, H.: Cross-border financial surveillance: a network perspective. J. Financ. Econ. Policy 3(3), 182–205 (2011)

    Article  Google Scholar 

  40. European Central Bank: Recent advances in modeling systemic risk using network analysis, January 2010

    Google Scholar 

  41. Fama, E.: Efficient capital market: a review of theory and empirical work. J. Financ. 25(2), 383–417 (1970)

    Article  Google Scholar 

  42. Gallegati, M., Keen, S., Lux, T., Ormerod, P.: Worrying trends in econophysics. Physica A 370, 1–6 (2006)

    Article  MathSciNet  Google Scholar 

  43. Georgescu-Roegen, N.: The Entropy Law and the Economic Process, Cambridge, MA: Harvard University Press (1971)

    Book  Google Scholar 

  44. Gligor, M., Ignat, M.: Econophysics: a new field for statistical physics? Interdiscip. Sci. Rev. 26(3), 183–190 (2001)

    Article  Google Scholar 

  45. Gromb, D., Vayanos, D.: Limits of arbitrage: the state of the theory. Annu. Rev. Financ. Econ. 2, 251–275 (2010)

    Article  Google Scholar 

  46. Haldane, A.: Rethinking the financial network. Speech delivered at the Financial Student Association, Amsterdam, April 2009

    Google Scholar 

  47. Hooker, C.: Asymptotics, reduction, and emergence. Br. J. Philos. Sci. 55, 435–479 (2004)

    Article  MathSciNet  Google Scholar 

  48. Inaoka, H, Ninomiya, T., Shimizu, T., Takayasu, H., Taniguchi, K.: Fractal network derived from banking transaction - an analysis of network structures formed by financial institutions. Bank of Japan Working Paper No. 04-E-04 (2004)

    Google Scholar 

  49. International Monetary Fund: Global Financial Stability Report, Chapter II on Assessing the Systemic Implications of Financial Linkages. IMF, Washington (2009)

    Google Scholar 

  50. Jevons, W.S.: Investigations in Currency and Finance. Macmillan, London (1884)

    Google Scholar 

  51. Johnson, N.F., Jefferies, P., Pak, M.H., Financial Market Complexity: What Physicists can Tell us About Market Behavior. Oxford University Press, Oxford (2003)

    Book  Google Scholar 

  52. Keim, D.B.: Financial market anomalies. The New Palgrave Dictionary of Economics, 2nd edn., Palgrave Macmillan (2008)

    Google Scholar 

  53. Kim, J.: Supervenience, emergence, realization, reduction. In: Loux, M., Zimmerman, D. (eds.) The Oxford Handbook of Metaphysics, pp. 556–584. Oxford University Press, Oxford (2003)

    Google Scholar 

  54. Kiyono, K., Struzik, Z.R., Yamamoto, Y.: Criticality and phase transition in stock-price fluctuations. Phys. Rev. Lett. 96, 068701-1–068701-4 (2006)

    Google Scholar 

  55. Latora, V., Marchiori, M.: The architecture of complex systems. In: Gell-Mann, M., Tsallis, C. (eds.) Nonextensive Entropy-Interdisciplinary Applications. Oxford University Press, Oxford (2004)

    Google Scholar 

  56. Lux, T.: The stable Paretian hypothesis and the frequency of large stock returns: an examination of major German stocks. Appl. Financ. Econ. 6(6), 463–475 (1996)

    Article  Google Scholar 

  57. Lux, T., Heitger, F.: Micro-simulations of financial markets and the stylized facts. In: Takayasu, H. (ed.) Empirical Science of Financial Fluctuations: The Advent of Econophysics, pp. 123–134. Springer, Berlin (2001)

    Google Scholar 

  58. Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397, 498–500 (1999)

    Article  Google Scholar 

  59. Mandelbrot, B.: The variation of certain speculative prices. J. Bus. 36, 394–419 (1963)

    Article  Google Scholar 

  60. Mandelbrot B.: Fractals and Scaling in Finance: Discontinuity, Concentration, Risk. Berlin: Springer (1997)

    Book  MATH  Google Scholar 

  61. Mantegna, R.N., Stanley, H.E.: Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  62. Markose, S., Giansante, S., Gatkowski, M., Shaghaghi, A.R.: Too interconnected to fail: financial networks of CDS and other credit enhancement obligations of US banks. University of Essex, Discussion Paper No 683 (2010)

    Google Scholar 

  63. Mayr, E.: Populations, Species, and Evolution. Harvard University Press, Cambridge (1970)

    Google Scholar 

  64. McCauley, J.L.: Dynamics of Markets: Econophysics and Finance. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  65. Michael, F., Johnson, M.D.: Financial market dynamics. Physica A 320, 525 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  66. Min, S.H., Lee, J., Han, I.: Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Syst. Appl. 31, 652–660 (2006)

    Article  Google Scholar 

  67. Mirowski, P.: More Heat Than Light: Economics as Social Physics, Physics as Nature’s Economics. Cambridge University Press, Cambridge (1989)

    Book  Google Scholar 

  68. Mitchell, W.C.: Business Cycles. University of California Press, Berkeley (1913)

    Google Scholar 

  69. Mitchell, M.: Complexity: A Guided Tour. Oxford University Press, New York (2009)

    Google Scholar 

  70. Nelson, R., Winter, S.: An Evolutionary Theory of Economic Change. Belknap Press of Harvard University Press, Cambridge (1982)

    Google Scholar 

  71. Nelson, R.: Argument, methodology, and fashion: reactions to a paper by Arora and Merges, Industrial and Corporate Change 14(6), 1235–1236 (2005)

    Article  Google Scholar 

  72. Newman, M.E.J.: Power laws, Pareto distributions, and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)

    Article  Google Scholar 

  73. Pareto, V.: Cours d’Economie Politique. Rouge, Lausanne (1897)

    Google Scholar 

  74. Perona, E.: The confused state of complexity economics: an ontological explanation (2007). http://www.aaep.org.ar/anales/works/works2004/Perona_paper_aaep2004.pdf

  75. Persaud, A.: Sending the herd off the cliff edge: the disturbing interaction between herding and market-sensitive risk management systems. J. Risk Financ. 2(1), 59–65 (2000)

    Article  Google Scholar 

  76. Persaud, A.: Liquidity Black Holes: Understanding, Quantifying and Managing Financial Liquidity. Risk Books, London (2003)

    Google Scholar 

  77. Pisarenko, V., Sornette, D.: New statistic for financial return distributions: power law or exponential? Physica A 366, 387–400 (2006)

    Article  Google Scholar 

  78. Plerou, V., Gopikrishnan, P., Gabaix, X., Stanley, H.E.: Quantifying stock-price response to demand fluctuations. Phys. Rev. E 66, 027104 (2002)

    Article  Google Scholar 

  79. Ponzi, A., Aizawa, Y.: Evolutionary financial market models. Phys. A Stat. Mech. Appl. 287, 507–523 (2000)

    Article  MathSciNet  Google Scholar 

  80. Prpper, M., van Lelyveld, I., Heijmans, R.: Towards a network description of interbank payment flows. DNB Working Paper No. 177, May 2008

    Google Scholar 

  81. Puhr, C., Schmitz, S.W.: Structure and stability in payment networks a panel data analysis of ARTIS simulations. In: Leinonen, H. (ed.) Simulation Analyses and Stress Testing of Payment Networks, Multiprint Ltd, Bank of Finland (2009)

    Google Scholar 

  82. Richardson, K.: Managing complex organizations: complexity thinking and the science and art of management. Corp. Financ. Rev. 13, 23–30 (2008)

    Google Scholar 

  83. Rickles, D.: Econophysics for philosophers. Stud. Hist. Philos. Mod. Phys. 38(4), 948–978 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  84. Rickles, D.: Econophysics and the complexity of financial markets. In: Collier, J., Hooker, C. (eds.) Handbook of the Philosophy of Science, Vol.10: Philosophy of Complex Systems. Elsevier/North-Holland, Amsterdam (2010)

    Google Scholar 

  85. Roehner, B.M.: Patterns of Speculation: A Study in Observational Econophysics. Cambridge University Press, Cambridge (2005)

    Google Scholar 

  86. Rosser, J.B. Jr.: Is a Transdisciplinary perspective on economic complexity possible? J. Econ. Behav. Organ. 75(1), 3–11 (2010)

    Article  Google Scholar 

  87. Rrdam, K.B., Bech, M.L.: The topology of Danish interbank money flows. FRU Working Paper 2009/01, University of Copenhagen (2009)

    Google Scholar 

  88. Shleifer, A.: Inefficient Markets: An Introduction to Behavioral Finance. Oxford University Press, Oxford (2000)

    Book  Google Scholar 

  89. Shleifer, A., Vishny, R.W.: The limits of arbitrage. J. Financ. 52(1), 35–55 (1997)

    Article  Google Scholar 

  90. Simon, H.: A behavioral model of rational choice. Q. J. Econ. 69(1), 99–118 (1955)

    Article  Google Scholar 

  91. Simon, H.: The Sciences of the Artificial, 2nd edn. MIT Press, Cambridge (1981)

    Google Scholar 

  92. Soramäki, K., Bech, M.L., Arnold, J., Glass, R.J., Beyeler, W.E.: The topology of interbank payment flows. Physica A 379, 317–333 (2007)

    Article  Google Scholar 

  93. Sornette, D.: Why Stock Markets Crash: Critical Events in Complex Financial Systems. Princeton University Press, Princeton (2003)

    Google Scholar 

  94. Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press, Oxford (2003)

    MATH  Google Scholar 

  95. Stanley, H.E., Afanasyev, V., Amaral, L.A.N., Buldyrev, S.V., Goldberger, A.L., Havlin, S., Leschhorn, H., Maass, P., Mantegna, R.N., Peng, C.K., Prince, P.A., Salinger, M.A., Stanley, M.H.R., Viswanathan, G.M.: Anomalous fluctuations in the dynamics of complex systems: from DNA and physiology to econophysics. Physica A 224, 302–321 (1996)

    Article  Google Scholar 

  96. Stanley, M.H.R., Amaral, L.A.N., Buldyrev, S.V., Havlin, S., Leschhorn, H., Maass, P., Salinger, M.A., Stanley, M.H.R., Stanley, H.E.: Can statistical physics contribute to the science of economics? Fractals 4(3), 415–425 (1996)

    Article  MATH  Google Scholar 

  97. Stanley, H.E., Amaral, L.A.N., Canning, D., Gopikrishnan, P., Lee, Y., Liu, Y.: Econophysics: can physicists contribute to the science of economics? Physica A 269, 156–169 (1999)

    Article  Google Scholar 

  98. Stanley, H.E., Amaral, L.A.N., Gopikrishnan, P., Plerou, V., Rosenow, B.: Quantifying empirical economic fluctuations using the organizing principles of scale invariance and universality. In: Takayasu, H. (ed.) Empirical Science of Financial Fluctuations: The Advent of Econophysics, pp. 3–11. Springer, Berlin (2001)

    Google Scholar 

  99. Stanley, H.E., Gabaix, X., Gopikrishnan, P., Plerou, V.: Economic fluctuations and statistical physics: the puzzle of large fluctuations. Nonlinear Dyn. 44, 329–340 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  100. Strogatz, S.H.: Norbert Wiener’s brain waves. In: Levin, S. (ed.) Frontiers in Mathematical Biology. Lecture Notes in Biomathematics, vol. 100, pp. 122–138. Springer, Berlin (1994)

    Google Scholar 

  101. Tam, K.Y.: Neural network models and the prediction of bank bankruptcy. Omega 19, 429–445 (1991)

    Article  Google Scholar 

  102. Tam, K.Y., Kiang, M.Y.: Managerial applications of neural networks: the case of bank failure predictions. Manag. Sci. 38, 926–947 (1992)

    Article  MATH  Google Scholar 

  103. Tay, F.E.H., Cao, L.: Application of support vector machines in financial time series forecasting. Omega 29, 309–317 (2001)

    Article  Google Scholar 

  104. Tesfatsion, L., Judd, K.L.: Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. North-Holland, Amsterdam (2006)

    Google Scholar 

  105. The Warwick Commission on International Financial Reform: In Praise of Unlevel Playing Fields, Report, University of Warwick (2010)

    Google Scholar 

  106. Veblen, T.: Why is Economics not an Evolutionary Science?. Quarterly Journal of Economics 12, 373–397 (1898)

    Article  Google Scholar 

  107. Zeidan, R., Richardson, K.: Complexity theory and the financial crisis: a critical review. Corp. Financ. Rev. 14, 20–32 (2010)

    Google Scholar 

  108. Zhang, Y.-C.: Evolving models of financial markets. Europhys. News 29(2), 51–54 (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Charilaos Mertzanis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Mertzanis, C. (2014). Complexity Analysis and Systemic Risk in Finance: Some Methodological Issues. In: Kalyagin, V., Pardalos, P., Rassias, T. (eds) Network Models in Economics and Finance. Springer Optimization and Its Applications, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-09683-4_11

Download citation

Publish with us

Policies and ethics