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

Author

Listed:
  • Kai Yang

    (Changchun University of Technology)

  • Luan Zhao

    (Changchun University of Technology)

  • Qian Hu

    (Changchun University of Technology)

  • Wenshan Wang

    (Changchun University of Technology)

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.

Suggested Citation

  • Kai Yang & Luan Zhao & Qian Hu & Wenshan Wang, 2024. "Bayesian Quantile Regression Analysis for Bivariate Vector Autoregressive Models with an Application to Financial Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 1939-1963, October.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:4:d:10.1007_s10614-023-10498-w
    DOI: 10.1007/s10614-023-10498-w
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