Bayesian Quantile Regression Analysis for Bivariate Vector Autoregressive Models with an Application to Financial Time Series

Kai Yang (), Luan Zhao, Qian Hu and Wenshan Wang
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Kai Yang: Changchun University of Technology
Luan Zhao: Changchun University of Technology
Qian Hu: Changchun University of Technology
Wenshan Wang: Changchun University of Technology

Computational Economics, 2024, vol. 64, issue 4, No 1, 1939-1963

Abstract: Abstract To capture the conditional correlations between bivariate financial responses at different quantile levels, this paper considers the Bayesian quantile regression for bivariate vector autoregressive models. With the well known location-scale mixture representation for the asymmetric Laplace distribution, a working likelihood is obtained. By introducing the latent variables, a new Gibbs sampling algorithm is developed for drawing the posterior samples for the parameters and latent variables. The numerical simulation implies that the Gibbs sampling algorithm converges fast and the Bayesian quantile estimators perform well. Finally, a real example is given to discuss the relationship between the Canadian dollar to U.S. dollar exchange rate and long term annual interest rate of Canada.

Keywords: Vector autoregressive models; Bayesian quantile regression; Gibbs sampling; Latent variable (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10498-w

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