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

TL;DR

This study examines how trade policy uncertainty influences stock-bond correlations in the US, showing that time-varying models incorporating TPU and political factors best capture and forecast these dynamics.

Contribution

It extends correlation modeling by integrating TPU and political cycle effects into advanced GARCH-based frameworks, revealing their significant impact on correlation behavior.

Findings

TPU significantly affects stock-bond correlation dynamics.

Time-varying models outperform constant correlation models.

DCC models with TPU and political effects have superior forecasting accuracy.

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.