Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach

Rangan Gupta and Christian Pierdzioch

Financial Innovation, 2023, vol. 9, issue 1, 1-22

Abstract: Abstract Because 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 effects 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.

Keywords: Oil price; Realized volatility; Economic conditions indexes; Quantile Lasso; Prediction models; C22; C53; E32; E66; Q41 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-022-00435-5

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DOI: 10.1186/s40854-022-00435-5

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