45 citations · 260 across the 33 of their papers we have counts for
7 papers · 1 filter
Statistical monitoring of European cross-border physical electricity flows using novel temporal edge network processes
Anna Malinovskaya, Rebecca Killick, Kathryn Leeming +1
Conventional modelling of networks evolving in time focuses on capturing variations in the network structure. However, the network might be static from the origin or experience onl…
Dynamic Spatiotemporal ARCH Models: Small and Large Sample Results
Philipp Otto, Osman Doğan, Süleyman Taşpınar
This paper explores the estimation of a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model. The log-volatility term in this model can depend on (i) t…
A Markov-switching spatio-temporal ARCH model
Tzung Hsuen Khoo, Dharini Pathmanathan, Philipp Otto +1
Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARC…
Spatiotemporal modelling of PM concentrations in Lombardy (Italy) -- A comparative study
Philipp Otto, Alessandro Fusta Moro, Jacopo Rodeschini +7
This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mi…
Spatial autoregressive fractionally integrated moving average model
Philipp Otto, Philipp Sibbertsen
In this paper, we introduce the concept of fractional integration for spatial autoregressive models. We show that the range of the dependence can be spatially extended or diminishe…
Spatial and Spatiotemporal Volatility Models: A Review
Philipp Otto, Osman Doğan, Süleyman Taşpınar +2
Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence i…
Network log-ARCH models for forecasting stock market volatility
Raffaele Mattera, Philipp Otto
This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock ma…