Dynamic spatiotemporal ARCH models
Dynamic spatiotemporal ARCH models
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DOI：
10.1080/17421772.2023.2254817
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发表时间：
2022-02
影响因子：
2.3
通讯作者：
Philipp Otto;Osman Doğan;Suleyman Taspinar
中科院分区：
文献类型：
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作者：
Philipp Otto;Osman Doğan;Suleyman Taspinar
摘要 地理参考数据的特点是由于地理邻近性而具有固有的空间依赖性。在本文中，我们引入了动态时空自回归条件异方差（ARCH）过程来描述（i）对数平方时间滞后结果变量的影响，时间效应，（ii）对数平方结果变量的空间滞后，空间效应，以及（iii）时空效应对结果变量波动性的影响。我们基于线性和二次矩条件推导了广义矩法（GMM）估计器。我们展示了 GMM 估计量的一致性和渐近正态性。在研究了模拟中的有限样本性能后，通过分析 1995 年至 2015 年柏林公寓价格的月度对数回报来证明该模型，我们发现了显着的波动溢出效应。
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.