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Linear and nonlinear market correlations: characterizing financial crises and portfolio optimization

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  • Alexander Haluszczynski
  • Ingo Laut
  • Heike Modest
  • Christoph Rath

Abstract

Pearson correlation and mutual information based complex networks of the day-to-day returns of US S&P500 stocks between 1985 and 2015 have been constructed in order to investigate the mutual dependencies of the stocks and their nature. We show that both networks detect qualitative differences especially during (recent) turbulent market periods thus indicating strongly fluctuating interconnections between the stocks of different companies in changing economic environments. A measure for the strength of nonlinear dependencies is derived using surrogate data and leads to interesting observations during periods of financial market crises. In contrast to the expectation that dependencies reduce mainly to linear correlations during crises we show that (at least in the 2008 crisis) nonlinear effects are significantly increasing. It turns out that the concept of centrality within a network could potentially be used as some kind of an early warning indicator for abnormal market behavior as we demonstrate with the example of the 2008 subprime mortgage crisis. Finally, we apply a Markowitz mean variance portfolio optimization and integrate the measure of nonlinear dependencies to scale the investment exposure. This leads to significant outperformance as compared to a fully invested portfolio.

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  • Alexander Haluszczynski & Ingo Laut & Heike Modest & Christoph Rath, 2017. "Linear and nonlinear market correlations: characterizing financial crises and portfolio optimization," Papers 1712.02661, arXiv.org.
    • Handle: RePEc:arx:papers:1712.02661
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    Cited by:

    1. Ben Amor, Souhir & Althof, Michael & Härdle, Wolfgang Karl, 2022. "Financial Risk Meter for emerging markets," Research in International Business and Finance, Elsevier, vol. 60(C).
    2. Cho, Younghwan & Song, Jae Wook, 2023. "Hierarchical risk parity using security selection based on peripheral assets of correlation-based minimum spanning trees," Finance Research Letters, Elsevier, vol. 53(C).
    3. Anwesha Sengupta & Shashankaditya Upadhyay & Indranil Mukherjee & Prasanta K. Panigrahi, 2022. "Describing the effect of influential spreaders on the different sectors of Indian market: a complex networks perspective," Papers 2303.05432, arXiv.org.
    4. Li, Yan & Jiang, Xiong-Fei & Tian, Yue & Li, Sai-Ping & Zheng, Bo, 2019. "Portfolio optimization based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 671-681.
    5. Ben Amor, Souhir & Althof, Michael & Härdle, Wolfgang Karl, 2021. "FRM Financial Risk Meter for Emerging Markets," IRTG 1792 Discussion Papers 2021-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Jaros{l}aw Gruszka & Janusz Szwabi'nski, 2023. "Portfolio Optimisation via the Heston Model Calibrated to Real Asset Data," Papers 2302.01816, arXiv.org.
    7. Gao, Hai-Ling & Mei, Dong-Cheng, 2019. "The correlation structure in the international stock markets during global financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    8. Liu, Xueyong & Jiang, Cheng, 2020. "The dynamic volatility transmission in the multiscale spillover network of the international stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    9. Yu, Zhongde & Huang, Yu & Fu, Zuntao, 2020. "Nonlinear strength quantifier based on phase correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).

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