Details
| Original language | English |
|---|---|
| Article number | 100823 |
| Number of pages | 16 |
| Journal | Spatial Statistics |
| Volume | 60 |
| Early online date | 2 Apr 2024 |
| Publication status | Published - Apr 2024 |
| Externally published | Yes |
Abstract
Keywords
- Real-estate prices, Multivariate spatiotemporal data, Volatility clustering, QML estimator, Conditional heteroscedasticity
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Environmental Science(all)
- Management, Monitoring, Policy and Law
- Mathematics(all)
- Statistics and Probability
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In: Spatial Statistics, Vol. 60, 100823, 04.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A Multivariate Spatial and Spatiotemporal ARCH Model
AU - Otto, P.
N1 - Publisher Copyright: © 2024 The Author(s)
PY - 2024/4
Y1 - 2024/4
N2 - This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spatial autoregressive spill-over effects, as they are usually present in geo-referenced data. Furthermore, spatial and temporal cross-variable effects in the conditional variance are explicitly modelled. We transform the model to a multivariate spatiotemporal autoregressive model using a log-squared transformation and derive a consistent quasi-maximum-likelihood estimator (QMLE). For finite samples and different error distributions, the performance of the QMLE is analysed in a series of Monte-Carlo simulations. In addition, we illustrate the practical usage of the new model with a real-world example. We analyse the monthly real-estate price returns for three different property types in Berlin from 2002 to 2014. We find weak (instantaneous) spatial interactions, while the temporal autoregressive structure in the market risks is of higher importance. Interactions between the different property types only occur in the temporally lagged variables. Thus, we see mainly temporal volatility clusters and weak spatial volatility spillovers.
AB - This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spatial autoregressive spill-over effects, as they are usually present in geo-referenced data. Furthermore, spatial and temporal cross-variable effects in the conditional variance are explicitly modelled. We transform the model to a multivariate spatiotemporal autoregressive model using a log-squared transformation and derive a consistent quasi-maximum-likelihood estimator (QMLE). For finite samples and different error distributions, the performance of the QMLE is analysed in a series of Monte-Carlo simulations. In addition, we illustrate the practical usage of the new model with a real-world example. We analyse the monthly real-estate price returns for three different property types in Berlin from 2002 to 2014. We find weak (instantaneous) spatial interactions, while the temporal autoregressive structure in the market risks is of higher importance. Interactions between the different property types only occur in the temporally lagged variables. Thus, we see mainly temporal volatility clusters and weak spatial volatility spillovers.
KW - Real-estate prices
KW - Multivariate spatiotemporal data
KW - Volatility clustering
KW - QML estimator
KW - Conditional heteroscedasticity
U2 - 10.48550/arXiv.2204.12472
DO - 10.48550/arXiv.2204.12472
M3 - Article
VL - 60
JO - Spatial Statistics
JF - Spatial Statistics
SN - 2211-6753
M1 - 100823
ER -