Research Article: The role of global economic policy uncertainty in long-run volatilities and correlations of U.S. industry-level stock returns and crude oil

Date Published: February 8, 2018

Publisher: Public Library of Science

Author(s): Honghai Yu, Libing Fang, Boyang Sun, Jacint Balaguer.


We investigate how Global Economic Policy Uncertainty (GEPU) drives the long-run components of volatilities and correlations in crude oil and U.S. industry-level stock markets. Using the modified generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) and dynamic conditional correlation mixed data sampling (DCC-MIDAS) specifications, we find that GEPU is positively related to the long-run volatility of Financials and Consumer Discretionary industries; however, it is negatively related to Information Technology, Materials, Telecommunication Services and Energy. Unlike the mixed role of GEPU in the long-run volatilities, the long-run correlations are all positively related to GEPU across the industries. Additionally, the rankings of the correlations of Energy and Materials are time-invariant and classified as high, with the little exception of the latter. The Consumer Staples industry is time-invariant in the low-ranking group. Our results are helpful to policy makers and investors with long-term concerns.

Partial Text

In recent years, much attention has been paid to the inter-relation among crude oil, stock market return, and economic policy uncertainty (EPU). EPU refers to the contribution of government policy makers to the uncertainty regarding fiscal, regulatory, or monetary policy [1]. It should be stressed that some industries are more sensitive to decisions driven by political events than others [2]. That is to say, EPU should drive the long-run volatilities of specific industry-level stock markets in different ways.

We use daily WTI spot prices and one-month futures prices for crude oil, obtained from the Energy Information Administration (EIA). The data of one-month futures contracts are obtained from the New York Mercantile Exchange (NYMEX). The sample period spans from January 2, 1997 to April 18, 2016. The reasons for using the one-month futures contracts data are as follows. First, temporary random noise affects oil spot prices much more than the futures prices [20]. Second, the equity returns of firms operating in oil exploration, refinery, and marketing are co-integrated with the one-month and four-month oil futures prices [27, 28]. Third, as concluded by [21, 29], the variability of the futures prices is a judgment of the efficiency of hedging activities of firms engaged in hedging. However, considering the fact that the majority of previous studies have preferred using the spot price [30–32], we use both the spot and the futures prices data, similar to [33]. We believe that spot prices reflect information available to the markets up to time t, and futures prices measure the sentiment of the market participants towards the short-term future.

The model in this paper contains two sections. The first concerns the heteroscedasticity process of the return series, and we use the EGARCH-MIDAS model to investigate the impact on conditional volatility. The second concerns the dynamic correlation between the two assets. The DCC-MIDAS model is employed to analyze the impact of GEPU on long-run volatility and correlations between oil and industries.

We provide the estimation results in the first subsection for the EGARCH-MIDAS model that relate the long-run volatilities to the GEPU index. In the last two subsections, we discuss the DCC-MIDAS specifications that focus on the long-run correlations and the rankings of the correlation, respectively.

We explore the impact of Global Economic Policy Uncertainty (GEPU) on the long-run volatility and correlation between the crude oil market and industry-level stock returns, based on the EGARCH-MIDAS and DCC-MIDAS models. A strand of the literature investigates the relationship between the oil and stock markets. However, the market-level analysis could mask differences among individual industries. Our findings are as follows.




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