Volatility forecasting under the political uncertainty of the second Trump presidency

This study examines the growing importance of the variance risk premium (VRP) in forecasting realized volatility during periods of heightened uncertainty. We propose a structured framework for the formal evaluation of the VIX1D index. Furthermore, using SPY data around the 2024 U.S. presidential election and Trump’s inauguration, we provide evidence that augmenting HAR models with option-implied VRP, tailored to specific forecasting horizons, significantly improves both in-sample fit and out-of-sample forecast accuracy. The results remain robust in the benchmarking procedure against models enhanced by VIX9D, VIX, and short-term uncertainty proxies. The implied economic value of forward-looking risk measures in environments with increased uncertainty is complemented by utility-based evaluation that demonstrates welfare gains even in the presence of transaction costs. Our findings underscore the critical role of the option-derived information and substantiate its academic and practical relevance in uncertain settings.