IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2209.00268.html
   My bibliography  Save this paper

Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes

Author

Listed:
  • Deborah Miori
  • Mihai Cucuringu

Abstract

We define data-driven macroeconomic regimes by clustering the relative performance in time of indices belonging to different asset classes. We then investigate lead-lag relationships within the regimes identified. Our study unravels market features characteristic of different windows in time and leverages on this knowledge to highlight market trends or risks that can be informative with respect to recurrent market developments. The framework developed also lays the foundations for multiple possible extensions.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2209.00268
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2209.00268
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew Ang & Geert Bekaert, 2004. "How Regimes Affect Asset Allocation," Financial Analysts Journal, Taylor & Francis Journals, vol. 60(2), pages 86-99, March.
    2. Christian Borghesi & Matteo Marsili & Salvatore Miccich`e, 2007. "Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode," Papers physics/0702106, arXiv.org.
    3. 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.
    4. F. Pozzi & T. Matteo & T. Aste, 2012. "Exponential smoothing weighted correlations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 85(6), pages 1-21, June.
    5. Gautier Marti & S'ebastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Papers 1603.04017, arXiv.org, revised Apr 2016.
    6. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    7. Nicol'o Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Risk diversification: a study of persistence with a filtered correlation-network approach," Papers 1410.5621, arXiv.org.
    8. Gautier Marti & Sébastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Post-Print hal-01400395, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yichi Zhang & Mihai Cucuringu & Alexander Y. Shestopaloff & Stefan Zohren, 2023. "Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models," Papers 2305.06704, arXiv.org, revised Sep 2023.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    3. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    4. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2018. "Dynamic correlations at different time-scales with empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 534-544.
    5. Musmeci, Nicoló & Aste, Tomaso & Di Matteo, T., 2015. "Relation between financial market structure and the real economy: comparison between clustering methods," LSE Research Online Documents on Economics 61644, London School of Economics and Political Science, LSE Library.
    6. Nicolo Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," Papers 1406.0496, arXiv.org, revised Jan 2015.
    7. Jeremy Turiel & Tomaso Aste, 2019. "Sector Neutral Portfolios: Long memory motifs persistence in market structure dynamics," Papers 1910.08628, arXiv.org, revised Feb 2021.
    8. Musmeci, Nicoló & Nicosia, Vincenzo & Aste, Tomaso & Di Matteo, Tiziana & Latora, Vito, 2017. "The multiplex dependency structure of financial markets," LSE Research Online Documents on Economics 85337, London School of Economics and Political Science, LSE Library.
    9. Jos'e Vin'icius de Miranda Cardoso & Jiaxi Ying & Daniel Perez Palomar, 2020. "Algorithms for Learning Graphs in Financial Markets," Papers 2012.15410, arXiv.org.
    10. Yonatan Berman & Eshel Ben-Jacob & Xin Zhang & Yoash Shapira, 2016. "Analyzing the Long Term Cohesive Effect of Sector Specific Driving Forces," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-16, March.
    11. Nicolò Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2017. "The Multiplex Dependency Structure of Financial Markets," Complexity, Hindawi, vol. 2017, pages 1-13, September.
    12. 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).
    13. Philipp Wirth & Francesca Medda & Thomas Schroder, 2024. "Longitudinal market structure detection using a dynamic modularity-spectral algorithm," Papers 2407.04500, arXiv.org.
    14. Alejandro Rodriguez Dominguez, 2022. "Portfolio Optimization based on Neural Networks Sensitivities from Assets Dynamics respect Common Drivers," Papers 2202.08921, arXiv.org, revised Dec 2022.
    15. Emeka Nkoro & Aham Kelvin Uko, 2016. "Exchange Rate and Inflation Volatility and Stock Prices Volatility: Evidence from Nigeria, 1986-2012," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 6(6), pages 1-4.
    16. Czujack, Corinna & Flôres Junior, Renato Galvão & Ginsburgh, Victor, 1995. "On long-run price comovements between paintings and prints," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 269, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    17. Sotirios Varelas, 2022. "Virtual Immersive Platforms as a Strategic Innovative Destination Marketing Tool in the COVID-19 Era," Sustainability, MDPI, vol. 14(19), pages 1-15, October.
    18. Loperfido, Nicola, 2010. "A note on marginal and conditional independence," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1695-1699, December.
    19. Hyunsoo Kang, 2022. "Impacts of Income Inequality and Economic Growth on CO 2 Emissions: Comparing the Gini Coefficient and the Top Income Share in OECD Countries," Energies, MDPI, vol. 15(19), pages 1-15, September.
    20. KAMKOUM, Arnaud Cedric, 2023. "The Federal Reserve’s Response to the Global Financial Crisis and its Effects: An Interrupted Time-Series Analysis of the Impact of its Quantitative Easing Programs," Thesis Commons d7pvg, Center for Open Science.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2209.00268. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.