Financial Markets and Portfolio Management

, Volume 29, Issue 2, pp 125–147 | Cite as

Handling risk-on/risk-off dynamics with correlation regimes and correlation networks

  • Jochen PapenbrockEmail author
  • Peter Schwendner


In this paper, we present a framework for detecting distinct correlation regimes and analyzing the emerging state dependences for a multi-asset futures portfolio from 1998 to 2013. These correlation regimes have been significantly different since the financial crisis of 2008 than they were previously; cluster tracking shows that asset classes are now less separated. We identify distinct “risk-on” and “risk-off” assets with the help of correlation networks. In addition to visualizing, we quantify these observations using suitable metrics for the clusters and correlation networks. The framework will be useful for financial risk management, portfolio construction, and asset allocation.


Regime switching Correlation regimes Clustering  Correlation networks Risk management Portfolio construction Asset allocation 

JEL Classification

C14 G11 G01 D85 



We thank Frank Fabozzi and an anonymous referee for valuable comments that significantly improved the quality of this article.


  1. Dose, C., Cincotti, S.: Application to index and enhanced-index tracking portfolio. Physica A 355(1), 145–151 (2005)CrossRefGoogle Scholar
  2. Dütsch, P.: Anwendung von Machine Learning Techniken im Quantitativen Trading. Unpublished Master’s thesis, Zurich University of Applied Research (2014)Google Scholar
  3. Efron, B.: Bootstrap methods: another look at the Jackknife. Ann. Stat. 7, 1–26 (1979)CrossRefGoogle Scholar
  4. Fabozzi, F.J., Focardi, S.M.: Diversification: should we be diversifying trends? J. Portf. Manag. 36(4), 1–4 (2010)CrossRefGoogle Scholar
  5. Fengler, M., Schwendner, P., Pilz, K.F.: Basket volatility and correlation. In: Volatility as an Asset Class, pp. 95–131. Incisive Media, London (2007)Google Scholar
  6. Fengler, M.R., Schwendner, P.: Quoting multiasset equity options in the presence of errors from estimating correlations. J. Deriv. 11(4), 43–54 (2004)CrossRefGoogle Scholar
  7. Fenn, D., Porter, M.A., Mucha, P.J., McDonald, M., Williams, S., Johnson, N.F., Jones, N.S.: Dynamical clustering of exchange rates. Quant. Finance 12(10), 1493–1520 (2012)CrossRefGoogle Scholar
  8. Flood, C.: Investors ignore flak over volatility ETPs. Financial Times April 29 (2012)Google Scholar
  9. Gower, J.D.: Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53, 325–338 (1966)CrossRefGoogle Scholar
  10. HSBC: Risk on–risk off: the full story. Currency Quant Special, HSBC Global Research, November (2010)Google Scholar
  11. Khandani, A.E., Lo, A.W.: What happened to the quants in August 2007? J. Invest. Manag. 5(4), 5–54 (2007)Google Scholar
  12. Laloux, L., Cizeau, P., Potters, M., Bouchaud, J.-P.: Random matrix theory. Risk 12(3), 69 (1999)Google Scholar
  13. Lee, W.: Risk on/risk off. J. Portf. Manag. 38(3), 28–39 (2012)CrossRefGoogle Scholar
  14. Lisi, F., Corazza, M.: Clustering financial data for mutual fund management. In: Perna, C., Sibillo, M. (eds.) Mathematical and statistical methods in insurance and finance. Springer, Milan (2008)Google Scholar
  15. Mantegna, R.N.: Hierarchical structure in financial markets. Euro. Phys. J. B 11, 193–197 (1999)CrossRefGoogle Scholar
  16. Münnix, M.C., Shimada, T., Schäfer, R., Leyvraz, F., Seligman, T.H., Guhr, T., Stanley, H.E.: Identifying states of a financial market. Nat. Sci. Rep. 2, 644 (2012)CrossRefGoogle Scholar
  17. Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, N., Guhr, T., Stanley, H.E.: Random matrix approach to cross correlations in financial data. Phys. Rev. E 65, 1–18 (2002)CrossRefGoogle Scholar
  18. Stopford, J.: Diversification in a correlated world. Investec (2012)Google Scholar
  19. Tola, V., Lillo, F., Gallegati, M., Mantegna, R.N.: Cluster analysis for portfolio optimization. J. Econ. Dyn. Control 32(1), 235–258 (2008)CrossRefGoogle Scholar
  20. Tumminello, M., Coronnello, C., Lillo, F., Micciche’, S., Mantegna, R.N.: Spanning trees and bootstrap reliability estimation in correlation based networks. Int. J. Bifurc. Chaos 17(7), 2319–2329 (2007)CrossRefGoogle Scholar
  21. Tumminello, M., Lillo, F.: Correlation, hierarchies, and networks in financial markets. J. Econ. Behav. Organ. 102(30), 10421–10426 (2010)Google Scholar

Copyright information

© Swiss Society for Financial Market Research 2015

Authors and Affiliations

  1. 1.PPI AGFrankfurtGermany
  2. 2.Firamis UGOberurselGermany
  3. 3.Center for Alternative Investments and Risk ManagementZurich University of Applied SciencesWinterthurSwitzerland

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