45 citations · 260 across the 33 of their papers we have counts for
7 papers · 1 filter
A Simple and Robust Multi-Fidelity Data Fusion Method for Effective Modeling of Citizen-Science Air Pollution Data
Camilla Andreozzi, Pietro Colombo, Philipp Otto
We propose a robust multi-fidelity Gaussian process for integrating sparse, high-quality reference monitors with dense but noisy citizen-science sensors. The approach replaces the…
Estimation of Spatial and Temporal Autoregressive Effects using LASSO - An Example of Hourly Particulate Matter Concentrations
Elkanah Nyabuto, Philipp Otto, Yarema Okhrin
We present an estimation procedure of spatial and temporal effects in spatiotemporal autoregressive panel data models using the Least Absolute Shrinkage and Selection Operator, LAS…
Exponential Spatiotemporal GARCH Model with Asymmetric Volatility Spillovers
Ariane Nidelle Meli Chrisko, Philipp Otto, Wolfgang Schmid
This paper introduces a spatiotemporal exponential generalised autoregressive conditional heteroscedasticity (spatiotemporal E-GARCH) model, extending traditional spatiotemporal GA…
Multivariate Low-Rank State-Space Model with SPDE Approach for High-Dimensional Data
Jacopo Rodeschini, Lorenzo Tedesco, Francesco Finazzi +2
This paper proposes a novel low-rank approximation to the multivariate State-Space Model. The Stochastic Partial Differential Equation (SPDE) approach is applied component-wise to…
A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for Volatility in Financial Networks
Atika Aouri, Philipp Otto
We introduce a heterogeneous spatiotemporal GARCH model for geostatistical data or processes on networks, e.g., for modelling and predicting financial return volatility across firm…
A Bayesian Spatio-Temporal Model of Temperature- and Humidity-Related Mortality Using High-Resolution Climate Data
Corinna Perchtold, Julia Eisenberg, Philipp Otto
In this study, we introduce a novel and comprehensive extension of a Bayesian spatio-temporal disease mapping model that explicitly accounts for gender-specific effects of meteorol…
Forecasting Oil Volatility through Network Models with GARCH-Informed Correlation Weights
Fayçal Djebari, Kahina Mehidi, Khelifa Mazouz +1
This study addresses the computational challenges of forecasting volatility in high-dimensional commodity markets. Building on the Network log-ARCH framework, we introduce a novel…