We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility-specific observed and latent factors. The model offers a more general network interpretation, making it applicable for studying various types of network spillovers. The primary innovation lies in incorporating volatility-specific latent factors into the dynamic spatiotemporal volatility model. Using Bayesian estimation via the Markov Chain Monte Carlo (MCMC) method, the model offers a robust framework for analyzing the spatial, temporal, and spatiotemporal effects of a log-squared outcome variable on its volatility. We recommend using the deviance information criterion (DIC) and a regularized Bayesian MCMC method to select the number of relevant factors in the model. The model's flexibility is demonstrated through two applications: a spatiotemporal model applied to the U.S. housing market and another applied to financial stock market networks, both highlighting the model's ability to capture varying degrees of interconnectedness. In both applications, we find strong spatial/network interactions with relatively stronger spillover effects in the stock market.

翻译：我们提出了一种动态时空波动率模型，该模型通过纳入空间、时间与时空溢出效应，以及波动率特定的观测与潜因子，扩展了传统方法。该模型提供了一种更通用的网络解释，使其适用于研究各类网络溢出效应。其主要创新在于将波动率特定的潜因子纳入动态时空波动率模型中。通过基于马尔可夫链蒙特卡洛（MCMC）方法的贝叶斯估计，该模型为分析对数平方结果变量对其波动率的空间、时间及时空效应提供了一个稳健的框架。我们建议使用偏差信息准则（DIC）和正则化贝叶斯MCMC方法来选择模型中相关因子的数量。该模型的灵活性通过两个应用实例得以展示：一个应用于美国房地产市场的时空模型，以及另一个应用于金融股票市场网络的模型，两者均凸显了该模型捕捉不同程度关联性的能力。在这两个应用中，我们都发现了强烈的空间/网络交互作用，其中股票市场的溢出效应相对更强。