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Trade uncertainty impact on stock-bond correlations: Insights from conditional correlation models

Demetrio Lacava, Edoardo Otranto

TL;DR

This paper investigates how Trade Policy Uncertainty (TPU) and the U.S. presidential cycle shape dynamic stock–bond correlations. It extends CCC, STCC, and DCC multivariate volatility models by incorporating TPU and a presidential dummy, using daily data from 2015–2025 for major stock indices and the 10-year Treasury, with a two-step GARCH framework expressed as $H_t = m{S}_t m{R}_t m{S}_t$. The results reject constant correlations and show correlations swing with TPU, with stronger comovements under Republican administrations; DCC specifications that include TPU and political effects deliver superior in-sample fit and forecasting performance. These findings imply that high TPU and regime-related policy shifts can erode diversification benefits of bonds, highlighting the need for models that jointly track volatility, TPU, and the presidential cycle in portfolio risk management.

Abstract

This paper investigates the impact of Trade Policy Uncertainty (TPU) on stock-bond correlation dynamics in the United States. Using daily data on major U.S. stock indices and the 10-year Treasury bond from 2015 to 2025, we estimate correlation within a two-step GARCH-based framework, relying on multivariate specifications, including Constant Conditional Correlation (CCC), Smooth Transition Conditional Correlation (STCC), and Dynamic Conditional Correlation (DCC) models. We extend these frameworks by incorporating TPU index and a presidential dummy to capture effects of trade uncertainty and government cycles. The findings show that constant correlation models are strongly rejected in favor of time-varying specifications. Both STCC and DCC models confirm TPU's central role in driving correlation dynamics, with significant differences across political regimes. DCC models augmented with TPU and political effects deliver the best in-sample fit and strongest forecasting performance, as measured by statistical and economic loss functions.

Trade uncertainty impact on stock-bond correlations: Insights from conditional correlation models

TL;DR

This paper investigates how Trade Policy Uncertainty (TPU) and the U.S. presidential cycle shape dynamic stock–bond correlations. It extends CCC, STCC, and DCC multivariate volatility models by incorporating TPU and a presidential dummy, using daily data from 2015–2025 for major stock indices and the 10-year Treasury, with a two-step GARCH framework expressed as . The results reject constant correlations and show correlations swing with TPU, with stronger comovements under Republican administrations; DCC specifications that include TPU and political effects deliver superior in-sample fit and forecasting performance. These findings imply that high TPU and regime-related policy shifts can erode diversification benefits of bonds, highlighting the need for models that jointly track volatility, TPU, and the presidential cycle in portfolio risk management.

Abstract

This paper investigates the impact of Trade Policy Uncertainty (TPU) on stock-bond correlation dynamics in the United States. Using daily data on major U.S. stock indices and the 10-year Treasury bond from 2015 to 2025, we estimate correlation within a two-step GARCH-based framework, relying on multivariate specifications, including Constant Conditional Correlation (CCC), Smooth Transition Conditional Correlation (STCC), and Dynamic Conditional Correlation (DCC) models. We extend these frameworks by incorporating TPU index and a presidential dummy to capture effects of trade uncertainty and government cycles. The findings show that constant correlation models are strongly rejected in favor of time-varying specifications. Both STCC and DCC models confirm TPU's central role in driving correlation dynamics, with significant differences across political regimes. DCC models augmented with TPU and political effects deliver the best in-sample fit and strongest forecasting performance, as measured by statistical and economic loss functions.
Paper Structure (10 sections, 12 equations, 4 figures, 9 tables)

This paper contains 10 sections, 12 equations, 4 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Tracking the Joint Evolution of Correlations and Trade Uncertainty
  3. Conditional Correlation Models
  4. Constant Conditional Correlation Models
  5. Smooth Transition Conditional Correlation Models
  6. Dynamic Conditional Correlation Models
  7. Evaluating Competitive Models
  8. In-sample Model Comparison
  9. Out-of-Sample Forecasting Comparison
  10. Concluding Remarks

Figures (4)

  • Figure 1: Monthly rolling T-Bond-S&P500 correlation (black line, left axis), Trade Policy Uncertainty (TPU) index (gray line, right axis), and Inauguration Day (vertical red line). Sample period: January 5, 2015 -- July 18, 2025.
  • Figure 2: ST-CC Estimated $S\&P500$-$T-Bond$ correlation (dotted-blue line), smooth transition function (black line), and Trade Policy Uncertainty (TPU) index (dashed-gray line). Sample period: January 5, 2015 -- February 24, 2023.
  • Figure 3: Panel a) estimated smooth transition function (black line) and Trade Policy Uncertainty (TPU) index (dashed-gray line) from the STCC-TUPE. Panel b) analytical smooth transition function for the Republican (black line) and Democratic (blue line) administrations. Sample period: January 5, 2015 -- February 24, 2023.
  • Figure 4: DCC (a) and DCC-TUPE (b) $S\&P500$-$T-Bond$ estimated correlations. Sample period: January 5, 2015 -- February 24, 2023.