Philipp Otto, Osman Doğan, Süleyman Taşpınar, Wolfgang Schmid, Anil K. Bera
arXiv 24 Aug 2023 · Econometrics · publishedJournal of Economic Surveys (2024) · 9 citations (OpenAlex)
arXiv:2308.13061 · PDF · DOI · OpenAlex · Extracted main text
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 in the volatility may arise due to spatial spillovers among locations; that is, if two locations are in close proximity, they can exhibit similar volatilities. In this paper, we aim to provide a comprehensive review of the recent literature on spatial and spatiotemporal volatility models. We first briefly review time series volatility models and their multivariate extensions to motivate their spatial and spatiotemporal counterparts. We then review various spatial and spatiotemporal volatility specifications proposed in the literature along with their underlying motivations and estimation strategies. Through this analysis, we effectively compare all models and provide practical recommendations for their appropriate usage. We highlight possible extensions and conclude by outlining directions for future research.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Engle, R. F (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation | 1.000 | 5 | 3 | 100% |
| 2 | Otto, P., Dogan, O., and Taspnar, S (2022) Dynamic spatiotemporal arch models self | 0.874 | 7 | 2 | 100% |
| 3 | Robinson, P. M (2009) Large‐sample inference on spatial dependence | 0.874 | 5 | 2 | 100% |
| 4 | Sato, T. and Matsuda, Y (2021) Spatial extension of generalized autoregressive conditional heteroskedasticity models | 0.874 | 5 | 2 | 100% |
| 5 | Francq, C. and Zakoian, J.-M (2019) GARCH models: structure, statistical inference and financial applications | 0.874 | 5 | 2 | 100% |
| 6 | Otto, P., Dogan, O., and Taspnar, S (2022) A dynamic spatiotemporal stochastic volatility model with an application to environmental risks self | 0.860 | 11 | 3 | 64% |
| 7 | LeSage, J. P. and Pace, R. K (2009) Introduction to Spatial Econometrics | 0.843 | 4 | 4 | 75% |
| 8 | Otto, P. and Schmid, W (2019) Spatial and spatiotemporal GARCH models – a unified approach self | 0.843 | 3 | 3 | 100% |
| 9 | Taspnar, S., Dogan, O., Chae, J., and Bera, A. K (2021) Bayesian inference in spatial stochastic volatility models: An application to house price returns in chicago self | 0.822 | 9 | 3 | 56% |
| 10 | Dogan, O. and Taspnar, S (2023) Bayesian inference in spatial garch models: an application to us house price returns | 0.822 | 6 | 2 | 83% |
Showing the top 10 of 126 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Forecasting Oil Volatility through Network Models with GARCH-Informed Correlation Weights | 0.644 | 2 | 2 |
| 2 | A Dynamic Spatiotemporal and Network ARCH Model with Common Factors | 0.405 | 1 | 1 |