45 citations · 260 across the 33 of their papers we have counts for
37 papers
Variable-Projection Sparse Functional Principal Component Analysis: Interpretable Functional Dimensionality Reduction with Applications to Raman Spectral Data
Han Ying Lim, Dharini Pathmanathan, Philipp Otto +1
Functional principal component analysis (FPCA) provides low-rank representations of functional data but generally produces dense components, making it difficult to identify the loc…
Neighbourhood-Based Generalized Dynamic Principal Components for Spatial Functional Data
Dharini Pathmanathan, Teck Xiang Seow, Philipp Otto +1
Regular-grid spatial functional datasets arise naturally in gridded environmental, oceanographic, climate, and remote-sensing applications, where each spatial location is associate…
Learning Volatility Dependence Networks in UK Equity Markets using Penalised Spatiotemporal ARCH Models
Elkanah Nyabuto, Philipp Otto
Spatiotemporal ARCH models capture temporal volatility persistence and cross-sectional dependence but typically require a predefined spatial weight matrix. This is restrictive in f…
Comparative Analysis of Spatiotemporal Volatility Models: An Empirical Study on Financial Network Series
Ariane N. Meli Chrisko, Jessie Li, Philipp Otto +1
Various spatiotemporal and network GARCH models have recently been proposed to capture volatility interactions, such as the transmission of market risk across financial networks. T…
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…
Simple yet effective: a comparative study of statistical models for yearly hurricane forecasting
Pietro Colombo, Raffaele Mattera, Philipp Otto
In this paper, we study the problem of forecasting the next year's number of Atlantic hurricanes, which is relevant in many fields of applications such as land-use planning, hazard…
A Dynamic Spatiotemporal and Network ARCH Model with Common Factors
Osman Doğan, Raffaele Mattera, Philipp Otto +1
We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with vola…
Multidimensional spatiotemporal clustering -- An application to environmental sustainability scores in Europe
Caterina Morelli, Simone Boccaletti, Paolo Maranzano +1
The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low-carbon intensity economy. However, companies located…
A review of regularised estimation methods and cross-validation in spatiotemporal statistics
Philipp Otto, Alessandro Fassò, Paolo Maranzano
This review article focuses on regularised estimation procedures applicable to geostatistical and spatial econometric models. These methods are particularly relevant in the case of…
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…
A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks
Philipp Otto, Osman Doğan, Süleyman Taşpınar
This article introduces a dynamic spatiotemporal stochastic volatility (SV) model with explicit terms for the spatial, temporal, and spatiotemporal spillover effects. Moreover, the…
Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy
Alessandro Fassò, Jacopo Rodeschini, Alessandro Fusta Moro +7
The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that…