Dynamic functional data analysis with non-parametric state space models |
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Authors: | Márcio Poletti Laurini |
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Affiliation: | 1. Department of Economics, FEA-RP USP, Ribeir?o Preto, SP, Brazilmplaurini@gmail.com |
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Abstract: | In this article, we introduce a new method for modelling curves with dynamic structures, using a non-parametric approach formulated as a state space model. The non-parametric approach is based on the use of penalised splines, represented as a dynamic mixed model. This formulation can capture the dynamic evolution of curves using a limited number of latent factors, allowing an accurate fit with a small number of parameters. We also present a new method to determine the optimal smoothing parameter through an adaptive procedure, using a formulation analogous to a model of stochastic volatility (SV). The non-parametric state space model allows unifying different methods applied to data with a functional structure in finance. We present the advantages and limitations of this method through simulation studies and also by comparing its predictive performance with other parametric and non-parametric methods used in financial applications using data on the term structure of interest rates. |
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Keywords: | functional data penalised splines MCMC Bayesian non-parametric methods term structure of interest rates |
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