Open Access
June 2014 Bayesian Analysis of the Functional-Coefficient Autoregressive Heteroscedastic Model
Xin-Yuan Song, Jing-Heng Cai, Xiang-Nan Feng, Xue-Jun Jiang
Bayesian Anal. 9(2): 371-396 (June 2014). DOI: 10.1214/14-BA865


In this paper, we propose a new model called the functional-coefficient autoregressive heteroscedastic (FARCH) model for nonlinear time series. The FARCH model extends the existing functional-coefficient autoregressive models and double-threshold autoregressive heteroscedastic models by providing a flexible framework for the detection of nonlinear features for both the conditional mean and conditional variance. We propose a Bayesian approach, along with the Bayesian P-splines technique and Markov chain Monte Carlo algorithm, to estimate the functional coefficients and unknown parameters of the model. We also conduct model comparison via the Bayes factor. The performance of the proposed methodology is evaluated via a simulation study. A real data set derived from the daily S&P 500 Composite Index is used to illustrate the methodology.


Download Citation

Xin-Yuan Song. Jing-Heng Cai. Xiang-Nan Feng. Xue-Jun Jiang. "Bayesian Analysis of the Functional-Coefficient Autoregressive Heteroscedastic Model." Bayesian Anal. 9 (2) 371 - 396, June 2014.


Published: June 2014
First available in Project Euclid: 26 May 2014

zbMATH: 1327.62166
MathSciNet: MR3217000
Digital Object Identifier: 10.1214/14-BA865

Keywords: Autoregressive heteroscedastic models , Bayes factor , Bayesian P-splines , MCMC methods , nonlinear time series

Rights: Copyright © 2014 International Society for Bayesian Analysis

Vol.9 • No. 2 • June 2014
Back to Top