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A multilevel factor approach for the analysis of CDS commonality and risk contribution

Author

Listed:
  • Carlos Vladimir Rodríguez-Caballero

    (Mexico Autonomous Institute of Technology (ITAM) and CREATES)

  • Massimiliano Caporin

    (University of Padova)

Abstract
We introduce a novel multilevel factor model that allows for the presence of global and pervasive factors, local factors and semi-pervasive factors, and that captures common features across subsets of the variables of interest. We develop a model estimation procedure and provide a simulation experiment addressing the consistency of our proposal. We complete the analyses by showing how our multilevel model might explain on the commonality across CDS premiums at the global level. In this respect, we cluster countries by either the Debt/GDP ratio or by sovereign ratings. We show that multilevel models are easier to interpret compared with factor models based on principal component analysis. Finally, we experiment how the multilevel model might allow the recovery of the risk contribution due to the latent factors within a basket of country CDS.

Suggested Citation

  • Carlos Vladimir Rodríguez-Caballero & Massimiliano Caporin, 2018. "A multilevel factor approach for the analysis of CDS commonality and risk contribution," CREATES Research Papers 2018-33, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2018-33
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    References listed on IDEAS

    as
    1. Caporin, Massimiliano & Pelizzon, Loriana & Ravazzolo, Francesco & Rigobon, Roberto, 2018. "Measuring sovereign contagion in Europe," Journal of Financial Stability, Elsevier, vol. 34(C), pages 150-181.
    2. Francis A. Longstaff & Jun Pan & Lasse H. Pedersen & Kenneth J. Singleton, 2011. "How Sovereign Is Sovereign Credit Risk?," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 75-103, April.
    3. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    4. Douglas, Rohan & Berndt, Antje & Duffie, Darrell & Ferguson, Mark, 2017. "Corporate Credit Risk Premia," Research Papers repec:ecl:stabus:3617, Stanford University, Graduate School of Business.
    5. M. Ayhan Kose & Christopher Otrok & Charles H. Whiteman, 2003. "International Business Cycles: World, Region, and Country-Specific Factors," American Economic Review, American Economic Association, vol. 93(4), pages 1216-1239, September.
    6. Ang, Andrew & Longstaff, Francis A., 2013. "Systemic sovereign credit risk: Lessons from the U.S. and Europe," Journal of Monetary Economics, Elsevier, vol. 60(5), pages 493-510.
    7. Augustin, Patrick, 2018. "The term structure of CDS spreads and sovereign credit risk," Journal of Monetary Economics, Elsevier, vol. 96(C), pages 53-76.
    8. Dong Hwan Oh & Andrew J. Patton, 2018. "Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 181-195, April.
    9. Fabozzi, Frank J. & Giacometti, Rosella & Tsuchida, Naoshi, 2016. "Factor decomposition of the Eurozone sovereign CDS spreads," Journal of International Money and Finance, Elsevier, vol. 65(C), pages 1-23.
    10. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April.
    11. T. Roncalli & G. Weisang, 2016. "Risk parity portfolios with risk factors," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 377-388, March.
    12. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    13. In Choi & Dukpa Kim & Yun Jung Kim & Noh‐Sun Kwark, 2018. "A multilevel factor model: Identification, asymptotic theory and applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 355-377, April.
    14. Hkiri, Besma & Hammoudeh, Shawkat & Aloui, Chaker & Shahbaz, Muhammad, 2018. "The interconnections between U.S. financial CDS spreads and control variables: New evidence using partial and multivariate wavelet coherences," International Review of Economics & Finance, Elsevier, vol. 57(C), pages 237-257.
    15. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    16. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    17. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    18. Nader Naifar & Shawkat Hammoudeh & Aviral Kumar Tiwari, 2019. "Do Energy and Banking CDS Sector Spreads Reflect Financial Risks and Economic Policy Uncertainty? A Time-Scale Decomposition Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 507-534, August.
    19. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    20. Antje Berndt & Rohan Douglas & Darrell Duffie & Mark Ferguson, 2018. "Corporate Credit Risk Premia [Fallen angels and price pressure]," Review of Finance, European Finance Association, vol. 22(2), pages 419-454.
    21. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
    22. Koutmos, Dimitrios, 2019. "Asset pricing factors and bank CDS spreads," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 19-41.
    23. Fender, Ingo & Hayo, Bernd & Neuenkirch, Matthias, 2012. "Daily pricing of emerging market sovereign CDS before and during the global financial crisis," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2786-2794.
    24. Pereira, John & Sorwar, Ghulam & Nurullah, Mohamed, 2018. "What drives corporate CDS spreads? A comparison across US, UK and EU firms," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 188-200.
    25. Kocsis, Zalan & Monostori, Zoltan, 2016. "The role of country-specific fundamentals in sovereign CDS spreads: Eastern European experiences," Emerging Markets Review, Elsevier, vol. 27(C), pages 140-168.
    26. Besma Hkiri & Shawkat Hammoudeh & Chaker Aloui, 2016. "Strength of co-movement between sector CDS indexes and relationship with major economic and financial variables over time and during investment horizons," Applied Economics, Taylor & Francis Journals, vol. 48(48), pages 4635-4654, October.
    27. Iván M. Rodríguez & Krishnan Dandapani & Edward R. Lawrence, 2019. "Measuring Sovereign Risk: Are CDS Spreads Better than Sovereign Credit Ratings?," Financial Management, Financial Management Association International, vol. 48(1), pages 229-256, March.
    28. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    29. Mariya Gubareva, 2019. "Weight of the Default Component of CDS Spreads: Avoiding Procyclicality in Credit Loss Provisioning Framework," Complexity, Hindawi, vol. 2019, pages 1-19, July.
    30. Augustin, Patrick & Tédongap, Roméo, 2016. "Real Economic Shocks and Sovereign Credit Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(2), pages 541-587, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.
    2. Gloria González‐Rivera & C. Vladimir Rodríguez‐Caballero & Esther Ruiz, 2024. "Expecting the unexpected: Stressed scenarios for economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 926-942, August.
    3. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    4. Gonzalez Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2021. "Expecting the unexpected: economic growth under stress," DES - Working Papers. Statistics and Econometrics. WS 32148, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Rodríguez-Caballero, Carlos Vladimir, 2022. "Energy consumption and GDP: a panel data analysis with multi-level cross-sectional dependence," Econometrics and Statistics, Elsevier, vol. 23(C), pages 128-146.
    6. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    7. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.

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    More about this item

    Keywords

    multilevel factor models; risk contribution; CDS risk factors;
    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
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F30 - International Economics - - International Finance - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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