Matteo Iacopini, Carlo R. M. A. Santagiustina
arXiv 24 Dec 2020 · Statistics — Applications · publishedJournal of the Royal Statistical Society Series A (Statistics in Society) (2020) · 1 citations (OpenAlex)
arXiv:2012.13267 · PDF · DOI · OpenAlex · Extracted main text
Count time series obtained from online social media data, such as Twitter, have drawn increasing interest among academics and market analysts over the past decade. Transforming Web activity records into counts yields time series with peculiar features, including the coexistence of smooth paths and sudden jumps, as well as cross-sectional and temporal dependence. Using Twitter posts about country risks for the United Kingdom and the United States, this paper proposes an innovative state space model for multivariate count data with jumps. We use the proposed model to assess the impact of public concerns in these countries on market systems. To do so, public concerns inferred from Twitter data are unpacked into country-specific persistent terms, risk social amplification events, and co-movements of the country series. The identified components are then used to investigate the existence and magnitude of country-risk spillovers and social amplification effects on the volatility of financial markets.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cathy WS Chen, Khemmanant Khamthong, and Sangyeol Lee (2019) Markov switching integer-valued generalized auto-regressive conditional heteroscedastic models for dengue counts | 0.644 | 2 | 2 | 100% |
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| 3 | Olivier Cappé, Eric Moulines, and Tobias Rydén (2005) Inference in Hidden Markov Models | 0.511 | 2 | 2 | 50% |
| 4 | Sylvia Frühwirth-Schnatter (2006) Finite mixture and Markov switching models | 0.511 | 2 | 2 | 50% |
| 5 | Christian Gouriéroux and Joann Jasiak (2006) Autoregressive Gamma processes | 0.511 | 2 | 1 | 100% |
| 6 | Andréas Heinen and Erick Rengifo (2007) Multivariate autoregressive modeling of time series count data using copulas | 0.511 | 2 | 1 | 100% |
| 7 | Fangfang Wang and Haonan Wang (2018) Modelling non-stationary multivariate time series of counts via common factors | 0.511 | 2 | 1 | 100% |
| 8 | Tevfik Aktekin, Nick Polson, Refik Soyer, et al (2018) Sequential Bayesian analysis of multivariate count data | 0.405 | 1 | 1 | 100% |
| 9 | Francesco D'Amuri and Juri Marcucci (2017) The predictive power of Google searches in forecasting US unemployment | 0.405 | 1 | 1 | 100% |
| 10 | Richard A Davis, Scott H Holan, Robert Lund, and Nalini Ravishanker (2016) Handbook of discrete-valued time series | 0.405 | 1 | 1 | 100% |
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