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

<div><p>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.</p></div

The long-run and total correlations of 10 industries and oil <i>future</i> price.

<p>This figure presents the long-run and total correlations of the 10 industries to the oil futures market, estimated from the DCC-MIDAS model with GEPU. The 10 GICS Level 1 industries are Consumer Discretionary (COND), Consumer Staples (CONS), Energy (ENRS), Financials (FINL), Health Care (HLTH), Industrials (INDU), Information Technology (INFT), Materials (MATR), Telecommunication Services (TELS), and Utilities (UTIL).</p

Results of the DCC-MIDAS-GEPU model.

<p>Results of the DCC-MIDAS-GEPU model.</p

Descriptive statistics of the long-run dynamic correlations and their rankings.

<p>Descriptive statistics of the long-run dynamic correlations and their rankings.</p

Descriptive statistics of the data.

<p>Descriptive statistics of the data.</p

Results of the EGARCH-MIDAS-GEPU model.

<p>Results of the EGARCH-MIDAS-GEPU model.</p

Characteristics of spatial clustering of travel demands to North Korea.

Characteristics of spatial clustering of travel demands to North Korea.</p

The number of travel demands to North Korea during 2011–2018.

The number of travel demands to North Korea during 2011–2018.</p

Fig 2 -

a: The average percentage of each month during 2011-2018. b: The percentage of each month from 2011 to 2018.</p

Distribution map of travel demands to North Korea (The administrative boundaries were obtained from the Chinese National Geographic Information Center (http://ngcc.sbsm.gov.cn), using Acrgis 10.6.1 for visual processing.

The figure is similar but not identical to the original image and for illustrative purposes only).</p