Journal of Computational Finance

One-dimensional Markov-functional models driven by a non-Gaussian driver

Jaka Gogala and Joanne Kennedy

  • New implementation of a one-dimensional non-Gaussian Markov-functional model (MFM)
  • Connection between copula theory and the one-dimensional MFMs
  • Description of the relationship between one-dimensional separable LMMs and MFMs
  • ‘Limited’ CEV process as a driver a one-dimensional MFM

The class of Markov-functional models (MFMs) provides a framework that can be used to define interest-rate models of finite dimension calibrated to any arbitrage-free formula for caplet or swaption prices. Because of their computational efficiency, one-factor MFMs are of particular interest. So far, the literature has focused on mod- els driven by a Gaussian process. The aim of this paper is to move away from this Gaussian assumption and to provide new algorithms that can be used to implement an MFM driven by a more general class of one-dimensional diffusion processes. We provide additional insight into the role of the driving process by presenting a simple copula-based criterion that can be used to distinguish between models. Finally, we offer further insight into the dynamics of one-dimensional MFMs by relating them to separable local-volatility Libor market models and demonstrate this with a practical example.

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