Feng Ma

Economics and Econometrics · 2013–2021

A researcher in the Science corpus: 15 papers; trusts 62 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Economics and Econometrics — 442 citations into it; #10,575 of 23,481 by trust here, #3,439 by citations
• Finance — 303 citations into it; #10,440 of 10,518 by trust here, #3,009 by citations
• Renewable Energy, Sustainability and the Environment — 78 citations into it; #5,166 of 5,212 by trust here, #2,963 by citations
• Management Science and Operations Research — 68 citations into it
• Statistical and Nonlinear Physics — 32 citations into it
• Molecular Biology — 14 citations into it
• Statistics and Probability — 14 citations into it
• Global and Planetary Change — 10 citations into it

Whom Feng Ma trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Eric Ghysels
2. Ole E. Barndorff-Nielsen
3. Torben G. Andersen
4. Yudong Wang
5. Fulvio Corsi
6. Tim Bollerslev
7. Yu Wei
8. Francis X. Diebold
9. N. Shephard
10. Andrew J. Patton

Who trusts Feng Ma overall

1. Chao Liang
2. Yu Wei
3. Jing Liu
4. Yaojie Zhang
5. Lu Wang
6. Elie Bouri
7. Li Liu
8. Xu Gong
9. Tao Li

Papers from Feng Ma's perspective

Ordered by how strongly the researchers Feng Ma trusts endorse each paper. Same corpus, a different order for every researcher.

1. Generalized autoregressive conditional heteroskedasticity
2. Power and bipower variation with stochastic volatility and jumps
3. Econometric analysis of realized volatility and its use in estimating stochastic volatility models
4. Dynamic Conditional Correlation
5. A Simple Long Memory Model of Realized Volatility
6. The distribution of realized stock return volatility
7. Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (Review of Economics and Statistics)
8. Dynamic Conditional Correlation a Simple Class of Multivariate GARCH Models
9. CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH
10. A Reality Check for Data Snooping

Their papers

• Asymmetric volatility spillovers between oil and stock markets: Evidence from China and the United States (2019) — 211 citations
• Forecasting realized volatility in a changing world: A dynamic model averaging approach (2016) — 276 citations
• Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors? (2019) — 262 citations
• Forecasting the prices of crude oil: An iterated combination approach (2018) — 146 citations
• Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role? (2021) — 51 citations
• Forecasting stock price volatility: New evidence from the GARCH-MIDAS model (2020) — 174 citations
• Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models (2020) — 228 citations
• Geopolitical risk and oil volatility: A new insight (2019) — 286 citations
• Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019) — 167 citations
• Economic policy uncertainty and the Chinese stock market volatility: Novel evidence (2019) — 149 citations
• Forecasting the oil futures price volatility: Large jumps and small jumps (2018) — 75 citations
• Forecasting the realized volatility of the oil futures market: A regime switching approach (2017) — 153 citations
• Multifractal detrended cross-correlation analysis of the oil-dependent economies: Evidence from the West Texas intermediate crude oil and the GCC stock markets (2014) — 50 citations
• Cross-correlations between West Texas Intermediate crude oil and the stock markets of the BRIC (2013) — 49 citations
• Multifractal detrended cross-correlation analysis between the Chinese stock market and surrounding stock markets (2013) — 102 citations