Jing Liu
Economics and Econometrics · with Ma · 2017–2020
A researcher in the Science corpus: 5 papers; trusts 28 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Economics and Econometrics — 151 citations into it; #20,428 of 23,481 by trust here, #10,133 by citations
- Finance — 98 citations into it
- Renewable Energy, Sustainability and the Environment — 31 citations into it
- Management Science and Operations Research — 21 citations into it
Whom Jing Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- P. R. Hansen
- Fulvio Corsi
- Tim Bollerslev
- Torben G. Andersen
- Jonathan H. Wright
- Ole E. Barndorff-Nielsen
- K. Cremers
- Yu Wei
- James H. Stock
- Lutz Kilian
Who trusts Jing Liu overall
Papers from Jing Liu's perspective
Ordered by how strongly the researchers Jing Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Generalized autoregressive conditional heteroskedasticity
- Econometric analysis of realized volatility and its use in estimating stochastic volatility models
- A Simple Long Memory Model of Realized Volatility
- A Simple Approximate Long-Memory Model of Realized Volatility
- Microstructure Noise, Realized Variance, and Optimal Sampling
- Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (Review of Economics and Statistics)
- Separating Microstructure Noise from Volatility
- Econometric Analysis of Realised Covariation: High Frequency Covariance, Regression and Correlation in Financial Economics
- The distribution of realized stock return volatility
- The VIX, the Variance Premium and Stock Market Volatility (Social Science Research Network)
Their papers
- Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty? (2017) — 238 citations
- Forecasting stock price volatility: New evidence from the GARCH-MIDAS model (2020) — 174 citations
- Geopolitical risk and oil volatility: A new insight (2019) — 286 citations
- Forecasting the oil futures price volatility: Large jumps and small jumps (2018) — 75 citations
- Forecasting the realized range-based volatility using dynamic model averaging approach (2017) — 52 citations