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

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Gogala, Jaka and Kennedy, Joanne E. (2019) One-dimensional Markov-functional models driven by a non-Gaussian driver. Journal of Computational Finance, 23 (3). pp. 61-100. doi:10.21314/JCF.2019.377

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Official URL: https://doi.org/10.21314/JCF.2019.377

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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: 9 December 2019
Dates:
DateEvent
9 December 2019Published
23 February 2019Accepted
Volume: 23
Number: 3
Page Range: pp. 61-100
DOI: 10.21314/JCF.2019.377
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
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