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

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  • Jochen Papenbrock
  • Peter Schwendner

Abstract

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. Copyright Swiss Society for Financial Market Research 2015

Suggested Citation

  • Jochen Papenbrock & Peter Schwendner, 2015. "Handling risk-on/risk-off dynamics with correlation regimes and correlation networks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(2), pages 125-147, May.
    • Handle: RePEc:kap:fmktpm:v:29:y:2015:i:2:p:125-147
    DOI: 10.1007/s11408-015-0248-2
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    Cited by:

    1. Wu, Tao & Sun, Xiaotong & Xu, Xin & Jia, Nanfei & Xuan, Siyuan, 2024. "New evidence of interdependence in forex markets: A connection of connection analysis," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    2. Ali Hirsa & Joerg Osterrieder & Branka Hadji-Misheva & Jan-Alexander Posth, 2021. "Deep reinforcement learning on a multi-asset environment for trading," Papers 2106.08437, arXiv.org.
    3. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    4. Deborah Miori & Mihai Cucuringu, 2022. "Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes," Papers 2209.00268, arXiv.org, revised Sep 2022.
    5. Bilal Ahmed Memon & Rabia Tahir, 2021. "Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 329-344.
    6. Heckens, Anton J. & Guhr, Thomas, 2022. "New collectivity measures for financial covariances and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    7. Packham, N. & Woebbeking, F., 2023. "Correlation scenarios and correlation stress testing," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 55-67.
    8. Andrea Di Iura, 2022. "Comparison of empirical and shrinkage correlation algorithm for clustering methods in the futures market," SN Business & Economics, Springer, vol. 2(8), pages 1-17, August.
    9. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.

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    More about this item

    Keywords

    Regime switching; Correlation regimes; Clustering ; Correlation networks; Risk management; Portfolio construction; Asset allocation; C14; G11; G01; D85;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G01 - Financial Economics - - General - - - Financial Crises
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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