Bayesian inference in spatial GARCH models: an application to US house price returns

Dogan O., Taspinar S.

SPATIAL ECONOMIC ANALYSIS, vol.18, no.3, pp.410-428, 2023 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.1080/17421772.2022.2123553
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Business Source Elite, Business Source Premier, EconLit, INSPEC
  • Page Numbers: pp.410-428
  • Keywords: spatial generalized autoregressive conditional heteroskedasticity (SGARCH), volatility, spatial autoregressive model, spatial dependence, Bayesian inference, Markov chain Monte Carlo (MCMC), house price returns, STOCHASTIC VOLATILITY, ARCH
  • Istanbul Technical University Affiliated: Yes


In this paper we consider a high-order spatial generalized autoregressive conditional heteroskedasticity (GARCH) model to account for the volatility clustering patterns observed over space. The model consists of a log-volatility equation that includes the high-order spatial lags of the log-volatility term and the squared outcome variable. We use a transformation approach to turn the model into a mixture of normals model, and then introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation approach coupled with a data-augmentation technique. Our simulation results show that the Bayesian estimator has good finite sample properties. We apply a first-order version of the spatial GARCH model to US house price returns at the metropolitan statistical area level over the period 2006Q1-2013Q4 and show that there is significant variation in the log-volatility estimates over space in each period.