A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks

TL;DR

This paper develops a novel dynamic spatiotemporal stochastic volatility model that captures spatial and temporal spillovers and applies it to environmental risk analysis, demonstrating its effectiveness with real data.

Contribution

The paper introduces a new Bayesian spatiotemporal SV model with explicit spillover effects and site-specific volatility, along with a MCMC estimation method.

Findings

Identifies significant spatial and temporal spillovers in environmental data.

Shows increased uncertainty in winter compared to summer in air quality.

Validates the model's performance through simulation studies.

Abstract

This article introduces a dynamic spatiotemporal stochastic volatility (SV) model 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 to distinguish between spatial and temporal interactions, while each location may have a different volatility level. We study the statistical properties of an outcome variable under this process and show that it introduces spatial dependence in the outcome variable. Further, we present a Bayesian estimation procedure 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, we apply the model in a highly relevant field, namely environmental risk modeling. 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, we show pronounced spatial and temporal spillovers and larger uncertainties/risks during the winter season compared to the summer season.