IDEAS home Printed from https://ideas.repec.org/p/pen/papers/25-009.html
   My bibliography  Save this paper

Clustered Network Connectedness:A New Measurement Frameworkwith Application to Global Equity Markets

Author

Listed:
  • Bastien Buchwalter

    (SKEMA Business School and Universite Cote d’Azur)

  • Francis X. Diebold

    (University of Pennsylvania & NBER)

  • Kamil Yilmaz

    (Koc University)

Abstract

We study the properties of macroeconomic survey forecast response averages as the number of survey respondents grows. Such averages are “portfolios” of forecasts. We characterize the speed and pattern of the gains from diversification and their eventual decrease with portfolio size (the number of survey respondents) in both (1) the key real-world data-based environment of the U.S. Survey of Professional Forecasters (SPF), and (2) the theoretical model-based environment of equicorrelated forecast errors. We proceed by proposing and comparing various direct and model-based “crowd size signature plots”, which summarize the forecasting performance of k-average forecasts as a function of k, where k is the number of forecasts in the average. We then estimate the equicorrelation model for growth and inflation forecast errors by choosing model parameters to minimize the divergence between direct and model-based signature plots. The results indicate near-perfect equicorrelation model fit for both growth and inflation, which we explicate by showing analytically that, under conditions, the direct and fitted equicorrelation model-based signature plots are identical at a particular model parameter configuration. We find that the gains from diversification are greater for inflation forecasts than for growth forecasts, but that both gains nevertheless decrease quite quickly, so that fewer SPF respondents than currently used may be adequate.

Suggested Citation

  • Bastien Buchwalter & Francis X. Diebold & Kamil Yilmaz, 2025. "Clustered Network Connectedness:A New Measurement Frameworkwith Application to Global Equity Markets," PIER Working Paper Archive 25-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Handle: RePEc:pen:papers:25-009
    as

    Download full text from publisher

    File URL: https://economics.sas.upenn.edu/system/files/working-papers/25-009%20PIER%20Paper%20Submission.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    3. Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
    4. Stéphane Bonhomme & Thibaut Lamadon & Elena Manresa, 2022. "Discretizing Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 90(2), pages 625-643, March.
    5. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    6. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    7. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    8. Kristin J. Forbes & Roberto Rigobon, 2002. "No Contagion, Only Interdependence: Measuring Stock Market Comovements," Journal of Finance, American Finance Association, vol. 57(5), pages 2223-2261, October.
    9. Christian Brownlees & Guðmundur Stefán Guðmundsson & Gábor Lugosi, 2022. "Community Detection in Partial Correlation Network Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 216-226, January.
    10. Mathieu Jacomy & Tommaso Venturini & Sebastien Heymann & Mathieu Bastian, 2014. "ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.
    11. Harold D. Chiang & Yuya Sasaki & Yulong Wang, 2023. "Genuinely Robust Inference for Clustered Data," Papers 2308.10138, arXiv.org, revised Jan 2025.
    12. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    13. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    14. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    15. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.
    16. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    Full references (including those not matched with items on IDEAS)

    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. Yi, Shuyue & Xu, Zishuang & Wang, Gang-Jin, 2018. "Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?," International Review of Financial Analysis, Elsevier, vol. 60(C), pages 98-114.
    2. Kamil Yilmaz, 2018. "Bank Volatility Connectedness in South East Asia," Koç University-TUSIAD Economic Research Forum Working Papers 1807, Koc University-TUSIAD Economic Research Forum.
    3. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    4. Okorie, David Iheke & Lin, Boqiang, 2022. "Givers never lack: Nigerian oil & gas asymmetric network analyses," Energy Economics, Elsevier, vol. 108(C).
    5. Thomas F. P. Wiesen & Paul M. Beaumont, 2024. "A joint impulse response function for vector autoregressive models," Empirical Economics, Springer, vol. 66(4), pages 1553-1585, April.
    6. Al-Nassar, Nassar S. & Assaf, Rima & Chaibi, Anis & Makram, Beljid, 2024. "The nexus between mineral, renewable commodities, and regional stock sectors during health and military crises," Resources Policy, Elsevier, vol. 96(C).
    7. Gomez-Gonzalez, Jose E. & Hirs-Garzon, Jorge & Uribe, Jorge M., 2022. "Spillovers beyond the variance: Exploring the higher order risk linkages between commodity markets and global financial markets," Journal of Commodity Markets, Elsevier, vol. 28(C).
    8. Bostanci, Gorkem & Yilmaz, Kamil, 2020. "How connected is the global sovereign credit risk network?," Journal of Banking & Finance, Elsevier, vol. 113(C).
    9. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    10. Ma, Rufei & Liu, Zhenhua & Zhai, Pengxiang, 2022. "Does economic policy uncertainty drive volatility spillovers in electricity markets: Time and frequency evidence," Energy Economics, Elsevier, vol. 107(C).
    11. Hachicha, Néjib & Ben Amar, Amine & Ben Slimane, Ikrame & Bellalah, Makram & Prigent, Jean-Luc, 2022. "Dynamic connectedness and optimal hedging strategy among commodities and financial indices," International Review of Financial Analysis, Elsevier, vol. 83(C).
    12. Magkonis, Georgios & Tsopanakis, Andreas, 2020. "The Financial Connectedness Between Eurozone Core And Periphery: A Disaggregated View," Macroeconomic Dynamics, Cambridge University Press, vol. 24(7), pages 1674-1699, October.
    13. Francis X. Diebold & Kamil Yilmaz, 2022. "On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness," Papers 2211.04184, arXiv.org, revised Jan 2023.
    14. Cheng, Tingting & Liu, Junli & Yao, Wenying & Zhao, Albert Bo, 2022. "The impact of COVID-19 pandemic on the volatility connectedness network of global stock market," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    15. Thomas F. P. Wiesen & Todd Gabe & Lakshya Bharadwaj, 2023. "Econometric connectedness as a measure of urban influence: evidence from Maine," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-16, December.
    16. Paflioti, Persa & Vitsounis, Thomas K. & Teye, Collins & Bell, Michael G.H. & Tsamourgelis, Ioannis, 2017. "Box dynamics: A sectoral approach to analyse containerized port throughput interdependencies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 396-413.
    17. Yu Chen & Jie Hu & Weiping Zhang, 2020. "Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(6), pages 78-100, November.
    18. Gomez-Gonzalez, Jose Eduardo & Hirs-Garzon, Jorge & Uribe, Jorge M., 2020. "Spillovers beyond the variance: exploring the natural gas and oil higher order risk linkages with the global financial markets," Working papers 46, Red Investigadores de Economía.
    19. Wen, Tiange & Wang, Gang-Jin, 2020. "Volatility connectedness in global foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
    20. Lee, Hahn Shik & Lee, Woo Suk, 2019. "Cross-regional connectedness in the Korean housing market," Journal of Housing Economics, Elsevier, vol. 46(C).

    More about this item

    Keywords

    Network; Centrality; Spillover; Contagion; Interdependence; Co-movement;
    All these keywords.

    JEL classification:

    • F01 - International Economics - - General - - - Global Outlook
    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pen:papers:25-009. 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: Administrator (email available below). General contact details of provider: https://edirc.repec.org/data/deupaus.html .

    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.