A0485
Title: A dynamic spatiotemporal stochastic volatility model with an application to environmental risks
Authors: Philipp Otto - University of Glasgow (United Kingdom) [presenting]
Osman Dogan - Technical University Istanbul (Turkey)
Suleyman Taspinar - Queens College (United States)
Abstract: A dynamic spatiotemporal stochastic volatility (SV) model is introduced with explicit terms for the spatial, temporal, and spatiotemporal spillover effects. Moreover, the model includes time-invariant site-specific constant log-volatility terms. Thus, this formulation allows distinguishment between spatial and temporal interactions, while each location may have a different volatility level. The statistical properties of an outcome variable are studied under this process and is shown that it introduces spatial dependence in the outcome variable. Further, a Bayesian estimation procedure is presented based on the Markov Chain Monte Carlo (MCMC) approach using a suitable data transformation. After providing simulation evidence on the proposed Bayesian estimator's performance, the model is applied in a highly relevant field, namely environmental risk modelling. Even though there are only a few empirical studies on environmental risks, previous literature undoubtedly demonstrated the importance of climate variation studies. For example, for local air quality in Northern Italy in 2021, pronounced spatial and temporal spillovers are shown and larger uncertainties/risks during the winter season are compared to the summer season.
