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A case study on different one-factor Cheyette models for short maturity caplet calibration

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  • Arun Kumar Polala
  • Bernhard Hientzsch

Abstract

In [1], we calibrated a one-factor Cheyette SLV model with a local volatility that is linear in the benchmark forward rate and an uncorrelated CIR stochastic variance to 3M caplets of various maturities. While caplet smiles for many maturities could be reasonably well calibrated across the range of strikes, for instance the 1Y maturity could not be calibrated well across that entire range of strikes. Here, we study whether models with alternative local volatility terms and/or alternative stochastic volatility or variance models can calibrate the 1Y caplet smile better across the strike range better than the model studied in [1]. This is made possible and feasible by the generic simulation, pricing, and calibration frameworks introduced in [1] and some new frameworks presented in this paper. We find that some model settings calibrate well to the 1Y smile across the strike range under study. In particular, a model setting with a local volatility that is piece-wise linear in the benchmark forward rate together with an uncorrelated CIR stochastic variance and one with a local volatility that is linear in the benchmark rate together with a correlated lognormal stochastic volatility with quadratic drift (QDLNSV) as in [2] calibrate well. We discuss why the later might be a preferable model. [1] Arun Kumar Polala and Bernhard Hientzsch. Parametric differential machine learning for pricing and calibration. arXiv preprint arXiv:2302.06682 , 2023. [2] Artur Sepp and Parviz Rakhmonov. A Robust Stochastic Volatility Model for Interest Rate Dynamics. Risk Magazine, 2023

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  • Arun Kumar Polala & Bernhard Hientzsch, 2024. "A case study on different one-factor Cheyette models for short maturity caplet calibration," Papers 2408.11257, arXiv.org.
    • Handle: RePEc:arx:papers:2408.11257
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    References listed on IDEAS

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    1. Arun Kumar Polala & Bernhard Hientzsch, 2023. "Parametric Differential Machine Learning for Pricing and Calibration," Papers 2302.06682, arXiv.org, revised Feb 2023.
    2. Nick Deguillaume & Riccardo Rebonato & Andrey Pogudin, 2013. "The nature of the dependence of the magnitude of rate moves on the rates levels: a universal relationship," Quantitative Finance, Taylor & Francis Journals, vol. 13(3), pages 351-367, February.
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    Cited by:

    1. Hardik Routray & Bernhard Hientzsch, 2024. "Enforcing asymptotic behavior with DNNs for approximation and regression in finance," Papers 2411.05257, arXiv.org.

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