Raffaele Mattera, Philipp Otto
arXiv 20 Mar 2023 · Statistics — Applications · publishedInternational Journal of Forecasting (2024) · 39 citations (OpenAlex)
arXiv:2303.11064 · PDF · DOI · OpenAlex · Extracted main text
This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock market. To improve the forecasting accuracy, the model integrates temporally lagged volatility information and information from adjacent nodes, which may instantaneously spill across the entire network. The model is also suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH model for processes to networks. This new approach is compared with independent univariate log-ARCH models. We could quantify the improvements due to the instantaneous network ARCH effects, which are studied for the first time in this paper. The edges are determined based on various distance and correlation measures between the time series. The performances of the alternative networks' definitions are compared in terms of out-of-sample accuracy. Furthermore, we consider ensemble forecasts based on different network definitions.
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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 | Otto, P., Dogan, O., and Taspnar, S (2022) Dynamic spatiotemporal ARCH models self | 0.874 | 7 | 2 | 100% |
| 2 | Sucarrat, G., Grnneberg, S., and Escribano, A (2016) Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown | 0.874 | 5 | 2 | 100% |
| 3 | Zhou, J., Li, D., Pan, R., and Wang, H (2020) Network GARCH model | 0.874 | 5 | 2 | 100% |
| 4 | Hansen, P. R., Lunde, A., and Nason, J. M (2011) The model confidence set | 0.843 | 3 | 3 | 100% |
| 5 | Diebold, F. X. and Mariano, R. S (2002) Comparing predictive accuracy | 0.811 | 4 | 2 | 100% |
| 6 | Fülle, M. J. and Otto, P (2022) Spatial GARCH models for unknown spatial locations-an application to financial stock returns self | 0.644 | 2 | 2 | 100% |
| 7 | Geweke, J (1986) Modeling the persistence of conditional variances: a comment | 0.644 | 2 | 2 | 100% |
| 8 | Maharaj, E. A., D'Urso, P., and Caiado, J (2019) Time series clustering and classification | 0.644 | 2 | 2 | 100% |
| 9 | Billio, M., Caporin, M., Frattarolo, L., and Pelizzon, L (2021) Networks in risk spillovers: A multivariate GARCH perspective | 0.585 | 3 | 1 | 100% |
| 10 | Caporin, M. and Paruolo, P (2015) Proximity-structured multivariate volatility models | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 69 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 | 1.000 | 9 | 4 |
| 2 | A Dynamic Spatiotemporal and Network ARCH Model with Common Factors | 0.737 | 3 | 2 |
| 3 | Spatial and Spatiotemporal Volatility Models: A Review | 0.405 | 1 | 1 |