Giampiero M. Gallo, Demetrio Lacava, Edoardo Otranto
arXiv 29 Jan 2026 · General Economics
arXiv:2601.21534 · PDF · DOI · OpenAlex · Extracted main text
This paper examines the dynamic relationship between electoral polls and indicators of economic and financial uncertainty during the last two U.S. presidential elections (2020 and 2024). Using daily polling data on Donald Trump and measures such as the Aruoba-Diebold-Scotti Business Conditions Index, the 5-year Breakeven Inflation Rate, the Trade Policy Uncertainty index, and the VIX, we estimate conditional correlation models to capture time-varying interactions. The analysis reveals that in 2020, correlations between polls and uncertainty measures were highly dynamic and event-driven, reflecting the influence of exogenous shocks (COVID-19, oil price collapse) and political milestones (primaries, debates). In contrast, during the 2024 campaign, correlations remained close to zero, stable, and largely unresponsive to shocks, suggesting that entrenched polarization and non-economic events (e.g., assassination attempt, candidate changes) muted the economic channel. The study highlights how the interplay between voter sentiment, financial markets, and uncertainty varies across electoral contexts, offering a methodological contribution through the application of Dynamic Conditional Correlation models to political data and policy-relevant insights on the conditions under which economic fundamentals influence electoral dynamics.
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 | Engle (2002) `Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity model… | 0.811 | 4 | 2 | 100% |
| 2 | Aruoba, Diebold \ Scotti (2009) `Real-time measurement of business conditions', Journal of Business & Economic Statistics 27(4), 417–427 | 0.644 | 2 | 2 | 100% |
| 3 | Baker, Bloom \ Davis (2016) `Measuring economic policy uncertainty', The Quarterly Journal of Economics 131(4), 1593–1636 | 0.644 | 2 | 2 | 100% |
| 4 | Bollerslev (1986) `Generalized autoregressive conditional heteroskedasticity', Journal of Econometrics 31, 307–327 | 0.644 | 2 | 2 | 100% |
| 5 | Bollerslev (1990) `Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch model', The Review of Economics an… | 0.644 | 2 | 2 | 100% |
| 6 | Caldara, Iacoviello, Molligo, Prestipino \ Raffo (2020) `The economic effects of trade policy uncertainty', Journal of Monetary Economics 109, 38–59 | 0.644 | 2 | 2 | 100% |
| 7 | Bauwens \ Otranto (2020) `Nonlinearities and regimes in conditional correlations with different dynamics', Journal of Econometrics 217(2), 496–522 | 0.511 | 2 | 1 | 100% |
| 8 | Engle (1982) `Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation', Econometrica 50, 987… | 0.511 | 2 | 1 | 100% |
| 9 | Abolghasemi \ Dimitrov (2021) `Determining the causality between US presidential prediction markets and global financial markets', International Journal of Fi… | 0.405 | 1 | 1 | 100% |
| 10 | Aielli (2013) `Dynamic conditional correlation: on properties and estimation', Journal of Business & Economic Statistics 31(3), 282–299 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 27 scored citations.