One-Dimensional Markov-Functional Models Driven by Non-Gaussian Markov Processes

40 Pages Posted: 10 Mar 2017

See all articles by Jaka Gogala

Jaka Gogala

University of Warwick

Joanne Kennedy

University of Warwick - Department of Statistics

Date Written: March 8, 2017

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 efficiency 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 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 Markov-functional models by relating them to separable local-volatility LIBOR market models and demonstrate this with a practical example.

Keywords: Constant Elasticity of Variance, Copula Theory, Interest Rate Models, Local-Volatility LIBOR Market Models, Markov-Functional Models

Suggested Citation

Gogala, Jaka and Kennedy, Joanne E., One-Dimensional Markov-Functional Models Driven by Non-Gaussian Markov Processes (March 8, 2017). Available at SSRN: https://ssrn.com/abstract=2929683 or http://dx.doi.org/10.2139/ssrn.2929683

Jaka Gogala (Contact Author)

University of Warwick ( email )

Gibbet Hill Rd.
Coventry, West Midlands CV4 8UW
United Kingdom

Joanne E. Kennedy

University of Warwick - Department of Statistics ( email )

Coventry CV4 7AL
United Kingdom

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