Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
| Editors | Michael Beer, Enrico Zio |
| Pages | 2685-2692 |
| Number of pages | 8 |
| ISBN (electronic) | 9789811127243 |
| Publication status | Published - 2020 |
Abstract
In this paper, the focus is on modeling local risks and uncertainties by generalized spatial autoregressive conditional heteroscedasticity (spGARCH) models. In contrast to temporal ARCH models, in which the distribution is known given the full information set of the prior periods, the distribution is not straightforward in spatial and spatiotemporal settings. However, spatial GARCH models allow for instantaneous dependence in the local variance. Thus, the models are suitable to model spatial risk clusters. Furthermore, spatial GARCH models can be used to account for local differences in model uncertainties, if they are considered as error process of any spatial model, like spatial autoregressive or spatial regression models. As the first conditional and unconditional moments are zero, spGARCH models are flexible tools for modeling residuals without influencing the mean model. The particular aim of this paper is to analyze the effect implied by the above mentioned spatial GARCH-type models. Hence, we inspect the so-called spatial spill-over effects in the second moments via simulation studies. These spill-over effects describe how an increase in the local risk of one location spreads out across all regions nearby.
Keywords
- Local uncertainties, Local volatility clusters, Spatial GARCH model, Spill-over effects
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
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Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. ed. / Michael Beer; Enrico Zio. 2020. p. 2685-2692.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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TY - GEN
T1 - Modeling spatial dependence in local risks and uncertainties
AU - Otto, P.
PY - 2020
Y1 - 2020
N2 - In this paper, the focus is on modeling local risks and uncertainties by generalized spatial autoregressive conditional heteroscedasticity (spGARCH) models. In contrast to temporal ARCH models, in which the distribution is known given the full information set of the prior periods, the distribution is not straightforward in spatial and spatiotemporal settings. However, spatial GARCH models allow for instantaneous dependence in the local variance. Thus, the models are suitable to model spatial risk clusters. Furthermore, spatial GARCH models can be used to account for local differences in model uncertainties, if they are considered as error process of any spatial model, like spatial autoregressive or spatial regression models. As the first conditional and unconditional moments are zero, spGARCH models are flexible tools for modeling residuals without influencing the mean model. The particular aim of this paper is to analyze the effect implied by the above mentioned spatial GARCH-type models. Hence, we inspect the so-called spatial spill-over effects in the second moments via simulation studies. These spill-over effects describe how an increase in the local risk of one location spreads out across all regions nearby.
AB - In this paper, the focus is on modeling local risks and uncertainties by generalized spatial autoregressive conditional heteroscedasticity (spGARCH) models. In contrast to temporal ARCH models, in which the distribution is known given the full information set of the prior periods, the distribution is not straightforward in spatial and spatiotemporal settings. However, spatial GARCH models allow for instantaneous dependence in the local variance. Thus, the models are suitable to model spatial risk clusters. Furthermore, spatial GARCH models can be used to account for local differences in model uncertainties, if they are considered as error process of any spatial model, like spatial autoregressive or spatial regression models. As the first conditional and unconditional moments are zero, spGARCH models are flexible tools for modeling residuals without influencing the mean model. The particular aim of this paper is to analyze the effect implied by the above mentioned spatial GARCH-type models. Hence, we inspect the so-called spatial spill-over effects in the second moments via simulation studies. These spill-over effects describe how an increase in the local risk of one location spreads out across all regions nearby.
KW - Local uncertainties
KW - Local volatility clusters
KW - Spatial GARCH model
KW - Spill-over effects
UR - http://www.scopus.com/inward/record.url?scp=85089198566&partnerID=8YFLogxK
M3 - Conference contribution
SP - 2685
EP - 2692
BT - Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
A2 - Beer, Michael
A2 - Zio, Enrico
ER -