IDEAS home Printed from https://ideas.repec.org/p/srk/srkwps/2021124.html
   My bibliography  Save this paper

A Multi-level Network Approach to Spillovers Analysis: An Application to the Maltese Domestic Investment Funds Sector

Author

Listed:
  • Meglioli, Francesco
  • Gauci, Stephanie

Abstract

In this paper we present a new approach to analyse the interconnectedness between a macro-level network and a local-level network. Our methodology is developed on the Diebold and Yilmaz connectedness measure and it considers the presence of entities within a global network which can influence other entities within their own local network but are not relevant enough to influence the entities which do not belong to the same local network. This methodology is then applied to the Maltese domestic investment funds sector and we find that a high-level correlation between the domestic funds can transmit higher spillovers to the local stock exchange index and to the government bond secondary market prices. Moreover, a high correlation among the Maltese domestic investment funds can increase their vulnerability to shocks stemming from financial indices, and therefore, investment funds may potentially become a shock transmission channel. JEL Classification: C32, C58, G10, G23

Suggested Citation

  • Meglioli, Francesco & Gauci, Stephanie, 2021. "A Multi-level Network Approach to Spillovers Analysis: An Application to the Maltese Domestic Investment Funds Sector," ESRB Working Paper Series 124, European Systemic Risk Board.
  • Handle: RePEc:srk:srkwps:2021124
    as

    Download full text from publisher

    File URL: https://www.esrb.europa.eu//pub/pdf/wp/esrb.wp124~0d1aaf1a99.en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Portes, Richard & , & D'Errico, Marco & Killeen, Neill & Luz, Vera & Peltonen, Tuomas & Urbano, Teresa, 2017. "Mapping the interconnectedness between EU banks and shadow banking entities," CEPR Discussion Papers 11919, C.E.P.R. Discussion Papers.
    2. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    3. Covi, Giovanni & Gorpe, Mehmet Ziya & Kok, Christoffer, 2021. "CoMap: Mapping Contagion in the Euro Area Banking Sector," Journal of Financial Stability, Elsevier, vol. 53(C).
    4. 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.
    5. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    6. Marco A. Espinosa‐Vega & Juan Solé, 2011. "Cross‐border financial surveillance: a network perspective," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 3(3), pages 182-205, August.
    7. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    8. Christian Manicaro & Joseph Falzon, 2017. "Hedge funds risk and connectedness," Journal of Asset Management, Palgrave Macmillan, vol. 18(4), pages 295-316, July.
    9. 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.
    10. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    11. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    12. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    13. Marco D'Errico & Tarik Roukny, 2017. "Compressing Over-the-Counter Markets," Papers 1705.07155, arXiv.org, revised Jun 2019.
    14. William B. English & Mico Loretan, 2000. "Evaluating \"correlation breakdowns\" during periods of market volatility," International Finance Discussion Papers 658, Board of Governors of the Federal Reserve System (U.S.).
    15. Brian H. Boyer & Michael S. Gibson & Mico Loretan, 1997. "Pitfalls in tests for changes in correlations," International Finance Discussion Papers 597, Board of Governors of the Federal Reserve System (U.S.).
    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. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    2. Francis X. Diebold & Kamil Yilmaz, 2016. "Trans-Atlantic Equity Volatility Connectedness: U.S. and European Financial Institutions, 2004–2014," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 81-127.
    3. Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Commodity Connectedness," Central Banking, Analysis, and Economic Policies Book Series, in: Enrique G. Mendoza & Ernesto Pastén & Diego Saravia (ed.),Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, edition 1, volume 25, chapter 4, pages 097-136, Central Bank of Chile.
    4. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306.
    5. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2016. "Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers," Journal of Financial Markets, Elsevier, vol. 27(C), pages 55-78.
    6. Fengler, Matthias R. & Gisler, Katja I.M., 2015. "A variance spillover analysis without covariances: What do we miss?," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 174-195.
    7. Wang, Gang-Jin & Xie, Chi & Zhao, Longfeng & Jiang, Zhi-Qiang, 2018. "Volatility connectedness in the Chinese banking system: Do state-owned commercial banks contribute more?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 57(C), pages 205-230.
    8. Gustavo Peralta, 2016. "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    9. Muneer Shaik & Mohd Ziaur Rehman, 2023. "The Dynamic Volatility Connectedness of Major Environmental, Social, and Governance (ESG) Stock Indices: Evidence Based on DCC-GARCH Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(1), pages 231-246, March.
    10. Gong, Jue & Wang, Gang-Jin & Zhou, Yang & Zhu, You & Xie, Chi & Foglia, Matteo, 2023. "Spreading of cross-market volatility information: Evidence from multiplex network analysis of volatility spillovers," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    11. Fasanya, Ismail & Akinbowale, Seun, 2019. "Modelling the return and volatility spillovers of crude oil and food prices in Nigeria," Energy, Elsevier, vol. 169(C), pages 186-205.
    12. Dai, Zhifeng & Tang, Rui & Zhang, Xiaotong, 2023. "A new multilayer network for measuring interconnectedness among the energy firms," Energy Economics, Elsevier, vol. 124(C).
    13. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    14. Costantini, Mauro & Maaitah, Ahmad & Mishra, Tapas & Sousa, Ricardo M., 2023. "Bitcoin market networks and cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    15. 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.
    16. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    17. 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.
    18. Oktay Ozkan & Salah Abosedra & Arshian Sharif & Andrew Adewale Alola, 2024. "Dynamic volatility among fossil energy, clean energy and major assets: evidence from the novel DCC-GARCH," Economic Change and Restructuring, Springer, vol. 57(3), pages 1-19, June.
    19. Lovcha, Yuliya & Pérez Laborda, Àlex, 2018. "Volatility Spillovers in a Long-Memory VAR: an Application to Energy Futures Returns," Working Papers 2072/307362, Universitat Rovira i Virgili, Department of Economics.
    20. Jozef Barunik & Mattia Bevilacqua & Radu Tunaru, 2022. "Asymmetric Network Connectedness of Fears," The Review of Economics and Statistics, MIT Press, vol. 104(6), pages 1304-1316, November.

    More about this item

    Keywords

    contagion; herding behaviour; interconnectedness; investment funds; Network model; systemic risk;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    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:srk:srkwps:2021124. 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: Official Publications (email available below). General contact details of provider: https://edirc.repec.org/data/esrbede.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.