 Estimation and Application of Fully Parametric Multifactor Quantile Regression with Dynamic CoefficientsFlorentina Paraschiv, Derek Bunn and Sjur Westgaard No 1607, Working Papers on Finance from University of St. Gallen, School of Finance Abstract: This paper develops and applies a novel estimation procedure for quantile regressions with time-varying coefficients based on a fully parametric, multifactor specification. The algorithm recursively filters the multifactor dynamic coefficients with a Kalman filter and parameters are estimated by maximum likelihood. The likelihood function is built on the Skewed-Laplace assumption. In order to eliminate the non-differentiability of the likelihood function, it is reformulated into a non-linear optimisation problem with constraints. A relaxed problem is obtained by moving the constraints into the objective, which is then solved numerically with the Augmented Lagrangian Method. In the context of an application to electricity prices, the results show the importance of modelling the time-varying features and the explicit multi-factor representation of the latent coefficients is consistent with an intuitive understanding of the complex price formation processes involving fundamentals, policy instruments and participant conduct. Keywords: Quantile Regression; Dynamic Coefficients; Parametric Estimation; Elec- tricity Prices (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:usg:sfwpfi:2016:07 Access Statistics for this paper More papers in Working Papers on Finance from University of St. Gallen, School of Finance Contact information at EDIRC. |
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