Osman Doğan, Raffaele Mattera, Philipp Otto, Süleyman Taşpınar
arXiv 21 Oct 2024 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2410.16526 · PDF · DOI · OpenAlex · Extracted main text
We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility-specific observed and latent factors. The model offers a more general network interpretation, making it applicable for studying various types of network spillovers. The primary innovation lies in incorporating volatility-specific latent factors into the dynamic spatiotemporal volatility model. Using Bayesian estimation via the Markov Chain Monte Carlo (MCMC) method, the model offers a robust framework for analyzing the spatial, temporal, and spatiotemporal effects of a log-squared outcome variable on its volatility. We recommend using the deviance information criterion (DIC) and a regularized Bayesian MCMC method to select the number of relevant factors in the model. The model's flexibility is demonstrated through two applications: a spatiotemporal model applied to the U.S. housing market and another applied to financial stock market networks, both highlighting the model's ability to capture varying degrees of interconnectedness. In both applications, we find strong spatial/network interactions with relatively stronger spillover effects in the stock market.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Trevor Park and George Casella (2008) The Bayesian Lasso | 0.811 | 4 | 2 | 100% |
| 2 | Markus J Fülle and Philipp Otto (2024) Spatial GARCH models for unknown spatial locations–an application to financial stock returns self | 0.737 | 3 | 2 | 100% |
| 3 | Raffaele Mattera and Philipp Otto (2024) Network log-ARCH models for forecasting stock market volatility self | 0.737 | 3 | 2 | 100% |
| 4 | David J. Spiegelhalter, Nicola G. Best, Bradley P. Carlin, and Angel… (2002) Bayesian measures of model complexity and fit | 0.644 | 2 | 2 | 100% |
| 5 | Sangjoon Kim, Neil Shephard, and Siddhartha Chib (1998) Stochastic volatility: Likelihood inference and comparison with ARCH models | 0.644 | 2 | 2 | 100% |
| 6 | Yasuhiro Omori, Siddhartha Chib, Neil Shephard, and Jouchi Nakajima (2007) Stochastic volatility with leverage: Fast and efficient likelihood inference | 0.644 | 2 | 2 | 100% |
| 7 | Matteo Barigozzi and Marc Hallin (2016) Generalized dynamic factor models and volatilities: recovering the market volatility shocks | 0.644 | 2 | 2 | 100% |
| 8 | Francis X Diebold and Kamil Ylmaz (2014) On the network topology of variance decompositions: Measuring the connectedness of financial firms | 0.644 | 2 | 2 | 100% |
| 9 | Osman Dogan and Süleyman Taspnar (2023) Bayesian inference in spatial GARCH models: an application to US house price returns | 0.644 | 2 | 2 | 100% |
| 10 | Philipp Otto and Wolfgang Schmid (2018) Spatiotemporal analysis of German real-estate prices self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 73 scored citations.