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Bank default indicators with volatility clustering

Author

Listed:
  • Turalay Kenc

    (TOBB University of Economics and Technology)

  • Emrah Ismail Cevik

    (Tekirdag Namik Kemal University)

  • Sel Dibooglu

    (University of Sharjah)

Abstract

We estimate default measures for US banks using a model capable of handling volatility clustering like those observed during the Global Financial Crisis (GFC). In order to account for the time variation in volatility, we adapted a GARCH option pricing model which extends the seminal structural approach of default by Merton (J Finance 29(2):449, 1974) and calculated “distance to default” indicators that respond to heightened market developments. With its richer volatility dynamics, our results better reflect higher expected default probabilities precipitated by the GFC. The diagnostics show that the model generally outperforms standard models of default and offers relatively good indicators in assessing bank failures.

Suggested Citation

  • Turalay Kenc & Emrah Ismail Cevik & Sel Dibooglu, 2021. "Bank default indicators with volatility clustering," Annals of Finance, Springer, vol. 17(1), pages 127-151, March.
    • Handle: RePEc:kap:annfin:v:17:y:2021:i:1:d:10.1007_s10436-020-00369-x
    DOI: 10.1007/s10436-020-00369-x
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    References listed on IDEAS

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    Cited by:

    1. Daniel Dimitrov & Sweder van Wijnbergen, 2022. "Quantifying Systemic Risk in the Presence of Unlisted Banks: Application to the Dutch Financial Sector," Tinbergen Institute Discussion Papers 22-034/VI, Tinbergen Institute.
    2. Dibooglu, Sel & Cevik, Emrah I. & Tamimi, Hussein A. Hassan Al, 2022. "Credit default risk in Islamic and conventional banks: Evidence from a GARCH option pricing model," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 396-411.
    3. Turalay Kenc & Emrah Ismail Cevik, 2021. "Estimating volatility clustering and variance risk premium effects on bank default indicators," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1373-1392, November.
    4. Ding, Shusheng & Cui, Tianxiang & Wu, Xiangling & Du, Min, 2022. "Supply chain management based on volatility clustering: The effect of CBDC volatility," Research in International Business and Finance, Elsevier, vol. 62(C).

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