IDEAS home Printed from https://ideas.repec.org/p/liv/livedp/202022.html
   My bibliography  Save this paper

Application of the Absorption Ratio to Illustrate Financial Connectedness and Interlinkages

Author

Listed:
  • Emma Apps

Abstract

This paper provides further evidence of the need to consider interlinkages and coupling within the financial system, particularly their impact upon portfolio management and in assessing risk exposures. This is done through application of the Absorption Ratio (AR) to ten European banks and insurance companies. In this case, the AR does not appear to act as an early warning indicator of market turmoil, which is inconsistent with the findings of Kritzman et al (2010 and 2014). However, one principal component is identified as explaining 70 to 80% of the variability in the assets’ returns for some of the period under review, in particular during the time of most severe financial crisis. A high AR suggests the stocks are more tightly coupled and provides evidence of interlinkages across two subsectors and a number of countries within Europe – thereby illustrating the extent of financial linkages and the high degree of correlation across markets and subsequent ramifications for portfolio managers.

Suggested Citation

  • Emma Apps, 2020. "Application of the Absorption Ratio to Illustrate Financial Connectedness and Interlinkages," Working Papers 202022, University of Liverpool, Department of Economics.
  • Handle: RePEc:liv:livedp:202022
    as

    Download full text from publisher

    File URL: https://www.liverpool.ac.uk/media/livacuk/schoolofmanagement/research/economics/Application,Absorption,Ratio.pdf
    File Function: First version, 2020
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pukthuanthong, Kuntara & Roll, Richard, 2009. "Global market integration: An alternative measure and its application," Journal of Financial Economics, Elsevier, vol. 94(2), pages 214-232, November.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. 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.
    4. Constanza Martinez & Manuel Ramirez, 2011. "International propagation of shocks: an evaluation of contagion effects for some Latin American countries," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 4(2), pages 213-233, December.
    5. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    6. Matthew S. Yiu & Wai-Yip Alex Ho & Lu Jin, 2010. "Dynamic Correlation Analysis of Financial Spillover to Asian and Latin American Markets in Global Financial Turmoil," Working Papers 1001, Hong Kong Monetary Authority.
    7. Susannah G Ellsworth & Bryan M Rabatic & Jie Chen & Jing Zhao & Jeffrey Campbell & Weili Wang & Wenhu Pi & Paul Stanton & Martha Matuszak & Shruti Jolly & Amy Miller & Feng-Ming Kong, 2017. "Principal component analysis identifies patterns of cytokine expression in non-small cell lung cancer patients undergoing definitive radiation therapy," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-10, September.
    8. Matthew Yiu & Wai-Yip Alex Ho & Daniel Choi, 2010. "Dynamic correlation analysis of financial contagion in Asian markets in global financial turmoil," Applied Financial Economics, Taylor & Francis Journals, vol. 20(4), pages 345-354.
    9. Laurini, Márcio Poletti & Ohashi, Alberto, 2015. "A noisy principal component analysis for forward rate curves," European Journal of Operational Research, Elsevier, vol. 246(1), pages 140-153.
    10. Joel Barber & Mark Copper, 2012. "Principal component analysis of yield curve movements," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(3), pages 750-765, July.
    11. Eugen Ivanov & Aleksey Min & Franz Ramsauer, 2017. "Copula-Based Factor Models for Multivariate Asset Returns," Econometrics, MDPI, vol. 5(2), pages 1-24, May.
    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. Liow, Kim Hiang & Song, Jeongseop, 2020. "Dynamic interdependence of ASEAN5 with G5 stock markets," Emerging Markets Review, Elsevier, vol. 45(C).
    2. Brownlees, Christian & Mesters, Geert, 2021. "Detecting granular time series in large panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 544-561.
    3. Nardo, M. & Ossola, E. & Papanagiotou, E., 2022. "Financial integration in the EU28 equity markets: Measures and drivers," Journal of Financial Markets, Elsevier, vol. 57(C).
    4. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    5. Wu, Jianhong, 2021. "Estimation of high dimensional factor model with multiple threshold-type regime shifts," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    6. Cristhian Mellado & Diego Escobari, 2015. "Virtual integration of financial markets: a dynamic correlation analysis of the creation of the Latin American Integrated Market," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1956-1971, April.
    7. Chaieb, Ines & Langlois, Hugues & Scaillet, Olivier, 2021. "Factors and risk premia in individual international stock returns," Journal of Financial Economics, Elsevier, vol. 141(2), pages 669-692.
    8. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    9. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015. "Risks of large portfolios," Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
    10. Yuefeng Han & Rong Chen & Dan Yang & Cun-Hui Zhang, 2020. "Tensor Factor Model Estimation by Iterative Projection," Papers 2006.02611, arXiv.org, revised May 2022.
    11. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
    12. Jushan Bai & Serena Ng, 2020. "Simpler Proofs for Approximate Factor Models of Large Dimensions," Papers 2008.00254, arXiv.org.
    13. Hyungsik Roger Moon & Martin Weidner, 2015. "Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects," Econometrica, Econometric Society, vol. 83(4), pages 1543-1579, July.
    14. Venetis, Ioannis & Ladas, Avgoustinos, 2022. "Co-movement and global factors in sovereign bond yields," MPRA Paper 115801, University Library of Munich, Germany.
    15. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    16. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2020. "IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude," Monash Econometrics and Business Statistics Working Papers 11/20, Monash University, Department of Econometrics and Business Statistics.
    17. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    18. Wang, Lu & Wu, Jianhong, 2022. "Estimation of high-dimensional factor models with multiple structural changes," Economic Modelling, Elsevier, vol. 108(C).
    19. Zhou, Zhongbao & Lin, Ling & Li, Shuxian, 2018. "International stock market contagion: A CEEMDAN wavelet analysis," Economic Modelling, Elsevier, vol. 72(C), pages 333-352.
    20. Xu Cheng & Zhipeng Liao & Frank Schorfheide, 2016. "Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1511-1543.

    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:liv:livedp:202022. 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: Rachel Slater (email available below). General contact details of provider: https://edirc.repec.org/data/mslivuk.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.