Libing Fang
Nanjing University
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Publication
Featured researches published by Libing Fang.
PLOS ONE | 2018
Honghai Yu; Libing Fang; Boyang Sun
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.
Applied Economics | 2018
Honghai Yu; Libing Fang; Sunqi Zhang; Donglei Du
ABSTRACT In this study, we investigate how US economic policy uncertainty (EPU) drives the long-run components of volatilities in industry-level stock markets. We use a modified specification of GARCH-MIDAS and find that EPU increases the long-run volatility of the industrials and materials industries and decreases it in 4 of the 10 industries considered here: consumer staples, healthcare, information technology and materials. In addition, we add a dummy variable for the political cycle (PLC) to study whether the relationship between EPU and the volatility of industry returns is significantly different under different political regimes. The results imply that a Republican presidency dampens the effects of EPU on the long-run volatility of the consumer staples, healthcare and information technology industries. We also decompose the aggregated EPU into 11 category-specific EPUs to explore the detailed relationship between category-specific EPU and long-run volatility driven by aggregate EPU. The results for the category-specific EPU are consistent with the findings for the aggregate EPU. In particular, the weakened effect of PLC on the relationship between EPU and the long-run volatility of industry-level returns is also confirmed by MIDAS regression with beta weight scheme.
Emerging Markets Review | 2017
Honghai Yu; Libing Fang; Boyang Sun; Donglei Du
Journal of Futures Markets | 2018
Libing Fang; Baizhu Chen; Honghai Yu; Yichuo Qian
International Review of Economics & Finance | 2018
Honghai Yu; Donglei Du; Libing Fang; Panpan Yan
Physica A-statistical Mechanics and Its Applications | 2018
Libing Fang; Yichuo Qian; Ying Chen; Honghai Yu
Physica A-statistical Mechanics and Its Applications | 2018
Honghai Yu; Libing Fang; Wencong Sun
Emerging Markets Review | 2018
Libing Fang; Boyang Sun; Huijing Li; Honghai Yu
Pacific-basin Finance Journal | 2018
Xindan Li; Honghai Yu; Libing Fang; Cheng Xiong
Finance Research Letters | 2017
Honghai Yu; Libing Fang; Donglei Du; Panpan Yan