Do Economic Conditions of U.S. States Predict the Realized Volatility of Oil-Price Returns? A Quantile Machine-Learning Approach

Rangan Gupta and Christian Pierdzioch

No 202216, Working Papers from University of Pretoria, Department of Economics

Abstract: Because the U.S. is a major player in the international oil market, it is interesting to study whether aggregate U.S. and state-level economic conditions can predict the subsequent realized volatility of oilprice returns. In order to address this research question, we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility (HAR-RV) model. For estimation of the models, we use the quantile-regression and quantile machine-learning (Lasso) estimators. Our estimation results shed light on the differential effects of economic conditions on the quantiles of the conditional distribution of realized volatility. Using weekly data for the period from April 1987 to December 2021, we document evidence of predictability at a biweekly and especially at a monthly horizon.

Keywords: Oil price; Realized volatility; Economic conditions indexes; Quantile Lasso; Prediction models (search for similar items in EconPapers)
JEL-codes: C22 C53 E32 E66 Q41 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2022-03
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202216

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