Gaussian models for Euro high grade government yields

Realdon, Marco (2017) Gaussian models for Euro high grade government yields. The European Journal of Finance, 23 (15). pp. 1468-1511. ISSN 1351-847X

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Abstract

This paper tests affine, quadratic and Black-type Gaussian models on Euro area triple A Government bond yields for maturities up to 30 years. Quadratic Gaussian models beat affine Gaussian models both in-sample and out-of-sample. A Black-type model best fits the shortest maturities and the extremely low yields since 2013, but worst fits the longest maturities. Even for quadratic models we can infer the latent factors from some yields observed without errors, which makes quasi-maximum likelihood (QML) estimation feasible. New specifications of quadratic models fit the longest maturities better than does the ‘classic’ specification of Ahn et al. [2002. ‘Quadratic Term Structure Models: Theory and Evidence.’ The Review of Financial Studies 15 (1): 243–288], but the opposite is true for the shortest maturities. These new specifications are more suitable to QML estimation. Overall quadratic models seem preferable to affine Gaussian models, because of superior empirical performance, and to Black-type models, because of superior tractability. This paper also proposes the vertical method of lines (MOL) to solve numerically partial differential equations (PDEs) for pricing bonds under multiple non-independent stochastic factors. ‘Splitting’ the PDE drastically reduces computations. Vertical MOL can be considerably faster and more accurate than finite difference methods.

Item Type:	Article
Uncontrolled Keywords:	affine Gaussian models, quadratic Gaussian models, Black model, vertical method oflines, sequential splitting, quasi-maximum likelihood, extended Kalman filter, G12, G13
Subjects:	N300 Finance
Department:	Faculties > Business and Law > Newcastle Business School
Depositing User:	Becky Skoyles
Date Deposited:	09 Jun 2016 09:36
Last Modified:	19 Nov 2019 09:04
URI:	http://nrl.northumbria.ac.uk/id/eprint/27070

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