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Spatiotemporal ARCH Models

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  • Takaki Sato
  • Yasumasa Matsuda

Abstract

This study proposes spatiotemporal extensions of time series autoregressive conditional heteroskedasticity (ARCH) models. We call spatiotemporally extended ARCH models as spatiotemporal ARCH (ST-ARCH) models. ST-ARCH models specify conditional variances given simultaneous observations and past observations, which constitutes a good contrast with time series ARCH models that specify conditional variances given past own observations. We have proposed two types of ST-ARCH models based on cross-sectional correlations between error terms. A spatial weight matrix based on Fama-French 3 factor models are used to quantify the closeness between stock prices. We estimate the parameters in ST-ARCH models by a two-step procedure of the quasi maximum likelihood estimation method. We demonstrate the empirical properties of the models by simulation studies and real data analysis of stock price data in the Japanese market.

Suggested Citation

  • Takaki Sato & Yasumasa Matsuda, 2018. "Spatiotemporal ARCH Models," DSSR Discussion Papers 82, Graduate School of Economics and Management, Tohoku University.
    • Handle: RePEc:toh:dssraa:82
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    File URL: http://hdl.handle.net/10097/00122621
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    Cited by:

    1. Philipp Otto & Wolfgang Schmid, 2021. "Generalized Spatial and Spatiotemporal ARCH Models," Papers 2106.10477, arXiv.org.

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