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Assessing swaption portfolios for prepayment risk mitigation. A parametric perspective

Author

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  • Monaco, Andrea

    (Center for Mathematical Economics, Bielefeld University)

  • Perrotta, Adamaria

    (Center for Mathematical Economics, Bielefeld University)

  • Sgarabottolo, Alessandro

    (Center for Mathematical Economics, Bielefeld University)

Abstract

We analyze the price behavior of Bermudan swaption portfolios used for hedging prepayment-driven interest rate risks in loan portfolios. We evaluate a variety of swaption portfolios across maturities and prepayment rates under various market conditions. Our findings reveal the existence of a parametric relation between swaption portfolio prices and the characteristics of the hedged loan. This relationship holds across different market conditions and valuation models, suggesting that one can swiftly adjust a swaption-based hedging strategy as loan portfolio characteristics evolve. This parametric approach allows financial institutions to reduce costs when assessing prepayment risks in their loan portfolios.

Suggested Citation

  • Monaco, Andrea & Perrotta, Adamaria & Sgarabottolo, Alessandro, 2025. "Assessing swaption portfolios for prepayment risk mitigation. A parametric perspective," Center for Mathematical Economics Working Papers 729, Center for Mathematical Economics, Bielefeld University.
  • Handle: RePEc:bie:wpaper:729
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    File URL: https://pub.uni-bielefeld.de/download/3006150/3006151
    File Function: First Version, 2023
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    References listed on IDEAS

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    1. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    2. Jay Cao & Jacky Chen & John Hull, 2020. "A neural network approach to understanding implied volatility movements," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1405-1413, September.
    3. Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Hedging of Derivatives Using Reinforcement Learning," Papers 2103.16409, arXiv.org.
    4. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    5. repec:bla:jfinan:v:44:y:1989:i:2:p:375-92 is not listed on IDEAS
    6. Manuel Moreno & Javier Navas, 2003. "On the Robustness of Least-Squares Monte Carlo (LSM) for Pricing American Derivatives," Review of Derivatives Research, Springer, vol. 6(2), pages 107-128, May.
    7. Bjork, Tomas, 2009. "Arbitrage Theory in Continuous Time," OUP Catalogue, Oxford University Press, edition 3, number 9780199574742.
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