Gupta, RanganPierdzioch, Christian2023-10-262023-10-262023-01Gupta, R., Pierdzioch, C. Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach. Financial Innovation 9, 24 (2023). https://doi.org/10.1186/s40854-022-00435-5.2199-4730 (online)10.1186/s40854-022-00435-5http://hdl.handle.net/2263/93088DATA AVAILABILITY: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable requestBecause the U.S. is a major player in the international oil market, it is interesting to study whether aggregate and state-level economic conditions can predict the subsequent realized volatility of oil price returns. To address this research question, we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility (HAR-RV) model. To estimate the models, we use quantile-regression and quantile machine learning (Lasso) estimators. Our estimation results highlights the differential efects of economic conditions on the quantiles of the conditional distribution of realized volatility. Using weekly data for the period April 1987 to December 2021, we document evidence of predictability at a biweekly and monthly horizon.en© The Author(s) 2023. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License.Oil priceRealized volatilityEconomic conditions indexesQuantile LassoPrediction modelsSDG-08: Decent work and economic growthUnited States (US)U.S. economic conditionsHeterogeneous autoregressive realized volatility (HAR-RV)Article