Gogala, Jaka and Kennedy, Joanne E. (2019) One-dimensional Markov-functional models driven by a non-Gaussian driver. Journal of Computational Finance . (In Press)

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Abstract

The class of Markov-functional models provide 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 eciency one-factor Markov-functional models are of particular interest. So far the literature has been focused on models 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 a Markov-functional model driven by a more general class of one-dimensional diusion 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 oer further insight into the dynamics of one-dimensional Markov-functional models by relating them to separable local-volatility LIBOR market models and demonstrate this with a practical example.

Item Type:	Journal Article
Divisions:	Faculty of Science > Statistics
Journal or Publication Title:	Journal of Computational Finance
Publisher:	Incisive Media Ltd.
ISSN:	1460-1559
Official Date:	23 February 2019
Dates:	Date Event 23 February 2019 Accepted
Date of first compliant deposit:	16 May 2019
Status:	Peer Reviewed
Publication Status:	In Press
Access rights to Published version:	Restricted or Subscription Access
Related URLs:	Publisher

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