Spatiotemporal dynamics of wind-speed volatility

TL;DR

This paper explores the spatiotemporal behavior of wind-speed volatility using a GARCH-type model across 141 stations in Northern Italy, highlighting the importance of spatial dependence in forecasting.

Contribution

It introduces a parsimonious spatiotemporal volatility framework combining GARCH dynamics with spatial mean models, enhancing wind-speed volatility analysis.

Findings

Proper spatial mean modeling improves residual behavior and inference.

Forecast performance depends on mean model flexibility and spatial dependence.

Vertical persistence and cross-height dependence increase with height.

Abstract

Wind-speed processes exhibit substantial temporal variability and spatial dependence, yet volatility dynamics across monitoring networks remain relatively unexplored. This study investigates the spatiotemporal behaviour of wind-speed volatility using daily observations from 141 stations in Northern Italy over 2016--2021, with measurements at 10 m and 100 m enabling the analysis of spatial and vertical dependence. We adopt a parsimonious spatiotemporal volatility framework based on GARCH-type dynamics, in which conditional variance depends on past local shocks and spatially aggregated information from neighbouring stations. The approach combines a spatial mean specification with structured volatility models using distance-based and directionally informed weight matrices. Results show that properly modelling spatial dependence in the mean is essential for well-behaved residuals and reliable inference. Forecast performance is strongly driven by the mean specification: flexible structures perform better when residual spatial dependence remains, while parsimonious distance-based models yield robust out-of-sample forecasts once spatial interactions are captured. Persistence increases with height, and a multivariate extension reveals cross-height dependence.