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Estimating asset correlations from stock prices or default rates - which method is superior?

Author

Listed:
  • Klaus Duellmann
  • Jonathan Küll

    (TH - Universität Karlsruhe)

  • Michael Kunisch

Abstract

This paper sets out to help explain why estimates of asset correlations based on equity prices tend to be considerably higher than estimates based on default rates. Resolving this empirical puzzle is highly important because, firstly, asset correlations are a key driver of credit risk and, secondly, both data sources are widely used to calibrate risk models of financial institutions. By means of a simulation study, we explore the hypothesis that differences in the correlation estimates are due to a substantial downward bias characteristic of estimates based on default rates. By varying the time horizon, the default probability, the asset correlation and the number of firms in the portfolio, we investigate these estimators in a systematic comparative study. Our results suggest that correlation estimates from equity returns are more efficient than those from default rates. This finding still holds if the model is misspecified such that asset correlations follow a Vasicek process which affects foremost the estimates from equity returns. The results lend support for the hypothesis that the downward bias of default-rate based estimates is an important although not the only factor to explain the differences in correlation estimates. Furthermore, our results help to quantify the estimation error of asset correlations dependent on the true values of default probability and asset correlation.

Suggested Citation

  • Klaus Duellmann & Jonathan Küll & Michael Kunisch, 2010. "Estimating asset correlations from stock prices or default rates - which method is superior?," Post-Print hal-00736734, HAL.
  • Handle: RePEc:hal:journl:hal-00736734
    DOI: 10.1016/j.jedc.2010.06.003
    Note: View the original document on HAL open archive server: https://hal.science/hal-00736734
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    Cited by:

    1. Kreis, Yvonne & Leisen, Dietmar P.J., 2018. "Systemic risk in a structural model of bank default linkages," Journal of Financial Stability, Elsevier, vol. 39(C), pages 221-236.
    2. Matteo Accornero & Giuseppe Cascarino & Roberto Felici & Fabio Parlapiano & Alberto Maria Sorrentino, 2018. "Credit risk in banks’ exposures to non‐financial firms," European Financial Management, European Financial Management Association, vol. 24(5), pages 775-791, November.
    3. Henry Penikas, 2023. "IRB Asset and Default Correlation: Rationale for the Macroprudential Mark-Ups to the IRB Risk-Weights," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-27, March.
    4. Byström, Hans, 2016. "The Currency Composition of Firms' Balance Sheets and its Effect on Asset Value Correlations and Capital Requirements," Working Papers 2016:1, Lund University, Department of Economics.
    5. Henry Penikas, 2020. "IRB Asset and Default Correlation: Rationale for the Macroprudential Add-ons to the Risk-Weights," Bank of Russia Working Paper Series wps56, Bank of Russia.
    6. Byström, Hans, 2017. "The currency composition of firms' balance sheets, asset value correlations, and capital requirements," Global Finance Journal, Elsevier, vol. 34(C), pages 89-99.
    7. Boudreault, Mathieu & Gauthier, Geneviève & Thomassin, Tommy, 2015. "Estimation of correlations in portfolio credit risk models based on noisy security prices," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 334-349.
    8. García-Céspedes, Rubén & Moreno, Manuel, 2014. "Estimating the distribution of total default losses on the Spanish financial system," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 242-261.
    9. Düllmann, Klaus & Koziol, Philipp, 2013. "Evaluation of minimum capital requirements for bank loans to SMEs," Discussion Papers 22/2013, Deutsche Bundesbank.
    10. Rainer Baule, 2021. "Credit risk in derivative securities: A simplified approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(5), pages 641-657, May.
    11. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    12. M. Dietsch & K. Düllmann & H. Fraisse & P. Koziol & C. Ott, 2016. "Support for the SME Supporting Factor - Multi-country empirical evidence on systematic risk factor for SME loans," Débats économiques et financiers 23, Banque de France.

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