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Spurious Cross-Sectional Dependence in Credit Spread Changes

Author

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  • Jaskowski, M.
  • McAleer, M.J.

Abstract

In order to understand the lingering credit risk puzzle and the apparent segmentation of the stock market from credit markets, we need to be able to assess the strength of the cross-sectional dependence in credit spreads. This turns out to be a non-trivial task due to the extreme data sparsity that is typical for any panel of credit spreads that is extracted from corporate bond transactions. The problem of data sparsity has led to some erroneous conclusions in the literature, including inferences that have been drawn from spurious cross-sectional dependence in credit spread changes. Understanding the pitfalls leads to a new and improved estimator of the latent factor in credit spread changes and its characteristics.

Suggested Citation

  • Jaskowski, M. & McAleer, M.J., 2018. "Spurious Cross-Sectional Dependence in Credit Spread Changes," Econometric Institute Research Papers EI 208-34, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:110016
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    References listed on IDEAS

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    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
    3. Pesaran, M. Hashem & Tosetti, Elisa, 2011. "Large panels with common factors and spatial correlation," Journal of Econometrics, Elsevier, vol. 161(2), pages 182-202, April.
    4. Ericsson, Jan & Jacobs, Kris & Oviedo, Rodolfo, 2009. "The Determinants of Credit Default Swap Premia," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(1), pages 109-132, February.
    5. Cremers, Martijn & Driessen, Joost & Maenhout, Pascal & Weinbaum, David, 2008. "Individual stock-option prices and credit spreads," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2706-2715, December.
    6. Schaefer, Stephen M. & Strebulaev, Ilya A., 2008. "Structural models of credit risk are useful: Evidence from hedge ratios on corporate bonds," Journal of Financial Economics, Elsevier, vol. 90(1), pages 1-19, October.
    7. Jing-Zhi Huang & Ming Huang, 2012. "How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 2(2), pages 153-202.
    8. M. Hashem Pesaran, 2015. "Testing Weak Cross-Sectional Dependence in Large Panels," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1089-1117, December.
    9. 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.
    10. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
    11. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    12. Carolina Castagnetti & Eduardo Rossi, 2013. "Euro Corporate Bond Risk Factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 372-391, April.
    13. Tomohiro Ando & Jushan Bai, 2017. "Clustering Huge Number of Financial Time Series: A Panel Data Approach With High-Dimensional Predictors and Factor Structures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1182-1198, July.
    14. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    15. Emanuel Moench & Serena Ng, 2011. "A hierarchical factor analysis of U.S. housing market dynamics," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 1-24, February.
    16. Harding, Matthew C., 2008. "Explaining the single factor bias of arbitrage pricing models in finite samples," Economics Letters, Elsevier, vol. 99(1), pages 85-88, April.
    17. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    18. repec:hal:journl:peer-00796743 is not listed on IDEAS
    19. Pierre Collin-Dufresn & Robert S. Goldstein & J. Spencer Martin, 2001. "The Determinants of Credit Spread Changes," Journal of Finance, American Finance Association, vol. 56(6), pages 2177-2207, December.
    20. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.
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    More about this item

    Keywords

    Credit spread puzzle; Market segmentation; Latent factors; Spurious cross-sectional dependence;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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