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A Data Science Approach to Predict the Impact of Collateralization on Systemic Risk

  • Sharyn O’Halloran
  • Nikolai NowaczykEmail author
  • Donal Gallagher
  • Vivek Subramaniam
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

In this chapter, we simulate and analyze the impact of financial regulations concerning the collateralization of derivative trades on systemic risk—a topic that has been vigorously discussed since the financial crisis in 2007/08. Experts often disagree on the efficacy of these regulations. Compounding this problem, banks regard their trade data required for a full analysis as proprietary. We adapt a simulation technology combining advances in graph theory to randomly generate entire financial systems sampled from realistic distributions with a novel open-source risk engine to compute risks in financial systems under different regulations. This allows us to consistently evaluate, predict, and optimize the impact of financial regulations on all levels—from a single trade to systemic risk—before it is implemented. The resulting data set is accessible to contemporary data science techniques like data mining, anomaly detection, and visualization. We find that collateralization reduces the costs of resolving a financial system in crisis, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations.

Keywords

Big data Graph theoretic models Data science Machine learning Python C++ Random graph generation Stochastic Linear Gauss-Markov model Monte Carlo simulation Financial risk analytics Systemic risk Collateralizations Variation margin Initial margin Open-source risk engine Financial regulation 

References

  1. 1.
    Adrian, T., Brunnermeier, M.K.: CoVaR. Am. Econ. Rev. 106(7), 1705–1741 (2016)Google Scholar
  2. 2.
    Andersen, L., Pykhtin, M., Sokol, A.: Does Initial Marign Eliminate Counterparty Risk?, risk.net (May 2017)Google Scholar
  3. 3.
    Anfuso, F., Aziz, D., Giltinan, P., Loukopoulos, K.: A Sound Modelling and Backtesting Framework for Forecasting Initial Margin Requirements, SSRN Pre-print (2016)Google Scholar
  4. 4.
    Bales, M., Johnson, S.: Graph theoretic modeling of large-scale semantic networks. J. Biomed. Inform. 39(4), 451–464 (2006)CrossRefGoogle Scholar
  5. 5.
    Bayati, M., Kim, J.-H., Saberi, A.: A sequential algorithm for generating random graphs. Algorithmica 58(4), 860–910 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Billio, M., Getmansky, M., Lo, A.W., Pelizzon, L.: Econometric Measures of Systemic Risk in the Finance and Insurance Sectors. NBER Working Paper 16223, NBER (2010)Google Scholar
  7. 7.
    Bisias, D., Flood, M., Lo, A.W., Valavanis, S.: A Survey of Systemic Risk Analytics. Office of Financial Research, Working Paper #0001 (January 5, 2012)Google Scholar
  8. 8.
    Britton, T., Deijfen, M., Martin-Löf, A.: Generating simple random graphs with prescribed degree distribution. J. Stat. Phys. 124(6), 1377–1397 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Caspers, P., Lichters, R.: Initial Margin Forecast–Bermudan Swaption Methodology and Case Study (February 27, 2018). Available at SSRN: https://ssrn.com/abstract=3132008
  10. 10.
    Cont, R., Moussa, A., Santos, E.: Network structure and systemic risk in banking systems. In Fouque, J., Langsam, J. (ed.): Handbook on Systemic Risk, pp. 327–368. Cambridge University Press, Cambridge (2013). https://doi.org/10.1017/CBO9781139151184.018 Google Scholar
  11. 11.
    Caspers, P., Giltinan, P., Lichters, R., Nowaczyk, N.: Forecasting initial margin requirements–a model evaluation. J. Risk Manage. Financ. Inst. 10(4), 365–394 (2017)Google Scholar
  12. 12.
    Erdős, P., Rényi, A.: On random graphs. Publ. Math. 6, 290–297 (1959)Google Scholar
  13. 13.
    Erdőos, P., Rényi, A. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17–61 (1960)Google Scholar
  14. 14.
    Lichters, R., Stamm, R., Gallagher, D.: Modern Derivatives Pricing and Credit Exposure Analysis: Theory and Practice of CVA and XVA Pricing, Exposure Simulation and Backtesting (Applied Quantitative Finance), Palgrave Macmillan, Basingstoke (2015)CrossRefGoogle Scholar
  15. 15.
    McWalter, T., Kienitz, J., Nowaczyk, N., Rudd, R., Acar, S.K.: Dynamic Initial Margin Estimation Based on Quantiles of Johnson Distributions, Working Paper (2018) Available at SSRN: https://ssrn.com/abstract=3147811
  16. 16.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)MathSciNetCrossRefGoogle Scholar
  17. 17.
    O’Halloran, S., Nowaczyk, N., Gallagher, D.: Big data and graph theoretic models: simulating the impact of collateralization on a financial system. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, pp. 1056–1064. (2017)Google Scholar
  18. 18.
    O’Halloran, S., Nowaczyk, N., Gallagher, D.: Big data and graph theoretic models: simulating the impact of collateralization on a financial system. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, pp. 1056–1064. ACM, New York (2017). https://doi.org/10.1145/3110025.3120989
  19. 19.
    Open Source Risk Engine. www.opensourcerisk.org. First release in October 2016

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sharyn O’Halloran
    • 1
  • Nikolai Nowaczyk
    • 2
    Email author
  • Donal Gallagher
    • 2
  • Vivek Subramaniam
    • 1
  1. 1.Columbia UniversityNew YorkUSA
  2. 2.Quaternion Risk ManagementLondonUK

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