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Multi-curve HJM modelling for risk management

Author

Listed:
  • Chiara Sabelli
  • Michele Pioppi
  • Luca Sitzia
  • Giacomo Bormetti

Abstract

We present a HJM approach to the projection of multiple yield curves developed to capture the volatility content of historical term structures for risk management purposes. Since we observe the empirical data at daily frequency and only for a finite number of time-to-maturity buckets, we propose a modelling framework which is inherently discrete. In particular, we show how to approximate the HJM continuous time description of the multi-curve dynamics by a Vector Autoregressive process of order one. The resulting dynamics lends itself to a feasible estimation of the model volatility-correlation structure and market risk-premia. Then, resorting to the Principal Component Analysis we further simplify the dynamics reducing the number of covariance components. Applying the constant volatility version of our model on a sample of curves from the Euro area, we demonstrate its forecasting ability through an out-of-sample test.

Suggested Citation

  • Chiara Sabelli & Michele Pioppi & Luca Sitzia & Giacomo Bormetti, 2018. "Multi-curve HJM modelling for risk management," Quantitative Finance, Taylor & Francis Journals, vol. 18(4), pages 563-590, April.
  • Handle: RePEc:taf:quantf:v:18:y:2018:i:4:p:563-590
    DOI: 10.1080/14697688.2017.1355104
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    Cited by:

    1. Atkins, Philip J. & Cummins, Mark, 2023. "Improved scalability and risk factor proxying with a two-step principal component analysis for multi-curve modelling," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1331-1348.
    2. Valerii Maltsev & Michael Pokojovy, 2021. "Applying Heath-Jarrow-Morton Model to Forecasting the US Treasury Daily Yield Curve Rates," Mathematics, MDPI, vol. 9(2), pages 1-25, January.

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