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Institutional Investors And The Dependence Structure Of Asset Returns

Author

Listed:
  • Rama Cont

    (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique)

  • Lakshithe Wagalath

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

Abstract

We propose a model of a financial market with multiple assets that takes into account the impact of a large institutional investor rebalancing its positions so as to maintain a fixed allocation in each asset. We show that feedback effects can lead to significant excess realized correlation between asset returns and modify the principal component structure of the (realized) correlation matrix of returns. Our study naturally links, in a quantitative manner, the properties of the realized correlation matrix — correlation between assets, eigenvectors and eigenvalues — to the sizes and trading volumes of large institutional investors. In particular, we show that even starting with uncorrelated "fundamentals", fund rebalancing endogenously generates a correlation matrix of returns with a first eigenvector with positive components, which can be associated to the market, as observed empirically. Finally, we show that feedback effects flatten the differences between the expected returns of assets and tend to align them with the returns of the institutional investor's portfolio, making this benchmark fund more difficult to beat, not because of its strategy but precisely because of its size and market impact.

Suggested Citation

  • Rama Cont & Lakshithe Wagalath, 2016. "Institutional Investors And The Dependence Structure Of Asset Returns," Post-Print hal-01562988, HAL.
    • Handle: RePEc:hal:journl:hal-01562988
    DOI: 10.1142/s0219024916500102
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    Citations

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    Cited by:

    1. Yann Braouezec & Lakshithe Wagalath, 2018. "Risk-Based Capital Requirements and Optimal Liquidation in a Stress Scenario [Testing macroprudential stress tests: the risk of regulatory risk weights]," Review of Finance, European Finance Association, vol. 22(2), pages 747-782.
    2. Guo Weilong & Minca Andreea & Wang Li, 2016. "The topology of overlapping portfolio networks," Statistics & Risk Modeling, De Gruyter, vol. 33(3-4), pages 139-155, December.
    3. Girardi, Giulio & Hanley, Kathleen W. & Nikolova, Stanislava & Pelizzon, Loriana & Sherman, Mila Getmansky, 2021. "Portfolio similarity and asset liquidation in the insurance industry," Journal of Financial Economics, Elsevier, vol. 142(1), pages 69-96.
    4. David Evangelista & Yuri Thamsten, 2020. "On finite population games of optimal trading," Papers 2004.00790, arXiv.org, revised Feb 2021.
    5. László Márkus & Ashish Kumar, 2021. "Modelling Joint Behaviour of Asset Prices Using Stochastic Correlation," Methodology and Computing in Applied Probability, Springer, vol. 23(1), pages 341-354, March.
    6. Gao, George P. & Moulton, Pamela C. & Ng, David T., 2017. "Institutional ownership and return predictability across economically unrelated stocks," Journal of Financial Intermediation, Elsevier, vol. 31(C), pages 45-63.
    7. Yann Braouezec & Lakshithe Wagalath, 2016. "Risk-based capital requirements and optimal liquidation in a stress scenario," Working Papers 2016-ACF-01, IESEG School of Management.
    8. Lakshithe Wagalath, 2016. "Feedback effects and endogenous risk in financial markets," Finance, Presses universitaires de Grenoble, vol. 37(2), pages 39-74.
    9. Paolo Guasoni & Kwok Chuen Wong, 2020. "Asset prices in segmented and integrated markets," Finance and Stochastics, Springer, vol. 24(4), pages 939-980, October.

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