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Dynamic spatiotemporal ARCH models

Author

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  • Philipp Otto
  • Osman Doğan
  • Süleyman Taşpınar

Abstract

Geo-referenced data are characterised by an inherent spatial dependence due to geographical proximity. In this paper, we introduce a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to describe the effects of (i) the log-squared time-lagged outcome variable, the temporal effect, (ii) the spatial lag of the log-squared outcome variable, the spatial effect, and (iii) the spatiotemporal effect on the volatility of an outcome variable. We derive a generalised method of moments (GMM) estimator based on the linear and quadratic moment conditions. We show the consistency and asymptotic normality of the GMM estimator. After studying the finite-sample performance in simulations, the model is demonstrated by analysing monthly log-returns of condominium prices in Berlin from 1995 to 2015, for which we found significant volatility spillovers.

Suggested Citation

  • Philipp Otto & Osman Doğan & Süleyman Taşpınar, 2024. "Dynamic spatiotemporal ARCH models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 19(2), pages 250-271, April.
    • Handle: RePEc:taf:specan:v:19:y:2024:i:2:p:250-271
    DOI: 10.1080/17421772.2023.2254817
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