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Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model

Author

Listed:
  • Ioannis Anagnostou

    (Computational Science Lab, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands
    Quantitative Analytics, ING Bank, Foppingadreef 7, 1102BD Amsterdam, The Netherlands)

  • Drona Kandhai

    (Computational Science Lab, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands
    Quantitative Analytics, ING Bank, Foppingadreef 7, 1102BD Amsterdam, The Netherlands)

Abstract
One of the key components of counterparty credit risk (CCR) measurement is generating scenarios for the evolution of the underlying risk factors, such as interest and exchange rates, equity and commodity prices, and credit spreads. Geometric Brownian Motion (GBM) is a widely used method for modeling the evolution of exchange rates. An important limitation of GBM is that, due to the assumption of constant drift and volatility, stylized facts of financial time-series, such as volatility clustering and heavy-tailedness in the returns distribution, cannot be captured. We propose a model where volatility and drift are able to switch between regimes; more specifically, they are governed by an unobservable Markov chain. Hence, we model exchange rates with a hidden Markov model (HMM) and generate scenarios for counterparty exposure using this approach. A numerical study is carried out and backtesting results for a number of exchange rates are presented. The impact of using a regime-switching model on counterparty exposure is found to be profound for derivatives with non-linear payoffs.

Suggested Citation

  • Ioannis Anagnostou & Drona Kandhai, 2019. "Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model," Risks, MDPI, vol. 7(2), pages 1-22, June.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:2:p:66-:d:239237
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    References listed on IDEAS

    as
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