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Stochastic Default Risk Estimation Evidence from the South African Financial Market

Author

Listed:
  • Mesias Alfeus

    (Stellenbosch University
    National Institute for Theoretical and Computational Sciences (NITheCS))

  • Kirsty Fitzhenry

    (Stellenbosch University)

  • Alessia Lederer

    (Stellenbosch University)

Abstract

This paper provides empirical studies of the estimation of defaultable bonds in the South African financial markets. The key objective is to estimate the unobservable factors affecting bond yields for South African major banks. The maximum likelihood approach is adopted for the estimation methodology. Multi-dimensional Cox–Ingersoll–Ross (CIR)-type factor models are considered and compared. Extended Kalman filtering techniques are employed in order to tackle the situation that the factors cannot be observed directly. We find empirical evidence that default risk varies with the business cycle, increased sharply in the South African financial market during COVID-19 and the $$\alpha$$ α -CIR model performs better than the classical CIR model.

Suggested Citation

  • Mesias Alfeus & Kirsty Fitzhenry & Alessia Lederer, 2024. "Stochastic Default Risk Estimation Evidence from the South African Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1715-1756, September.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:3:d:10.1007_s10614-023-10481-5
    DOI: 10.1007/s10614-023-10481-5
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    References listed on IDEAS

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