Details about Yudong Wang

Access statistics for papers by Yudong Wang.

Last updated 2024-11-06. Update your information in the RePEc Author Service.

Short-id: pwa928

Working Papers

2018

1. Forecasting Stock Returns: A Predictor-Constrained Approach
   Working Papers, Brandeis University, Department of Economics and International Business School
   Also in Working Papers, Brandeis University, Department of Economics and International Business School (2017) View citations (1)
   See also Journal Article Forecasting stock returns: A predictor-constrained approach, Journal of Empirical Finance, Elsevier (2020) View citations (18) (2020)

Journal Articles

2024

1. Abnormal temperature and the cross-section of stock returns in China
   International Review of Financial Analysis, 2024, 94, (C) View citations (1)
2. Crude oil futures and the short-term price predictability of petroleum products
   Energy, 2024, 307, (C)
3. Exploiting the sentiments: A simple approach for improving cross hedging effectiveness
   Energy Economics, 2024, 134, (C)
4. Forecasting carbon prices under diversified attention: A dynamic model averaging approach with common factors
   Energy Economics, 2024, 133, (C)
5. Forecasting crude oil market volatility: A comprehensive look at uncertainty variables
   International Journal of Forecasting, 2024, 40, (3), 1022-1041 View citations (3)
6. Forecasting oil futures returns with news
   Energy Economics, 2024, 134, (C)
7. Forecasting the equity premium using weighted regressions: Does the jump variation help?
   Empirical Economics, 2024, 66, (5), 2049-2082
8. Forecasting the volatility of crude oil basis: Univariate models versus multivariate models
   Energy, 2024, 295, (C)
9. Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint
   Journal of Forecasting, 2024, 43, (2), 309-325
10. Modeling and forecasting stock return volatility using the HARGARCH model with VIX information
    Journal of Futures Markets, 2024, 44, (8), 1383-1403
11. Modelling and forecasting crude oil price volatility with climate policy uncertainty
    Palgrave Communications, 2024, 11, (1), 1-10 View citations (2)
12. Oil information uncertainty and aggregate market returns: A natural experiment based on satellite data
    Journal of Financial Markets, 2024, 70, (C)
13. Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?
    Journal of Forecasting, 2024, 43, (3), 567-582
14. Solving the Forecast Combination Puzzle Using Double Shrinkages
    Oxford Bulletin of Economics and Statistics, 2024, 86, (3), 714-741
15. The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns
    Journal of Futures Markets, 2024, 44, (4), 557-584 View citations (1)

2023

16. Asymmetric spillover of geopolitical risk and oil price volatility: A global perspective
    Resources Policy, 2023, 83, (C) View citations (9)
17. Climate risk exposure and the cross-section of Chinese stock returns
    Finance Research Letters, 2023, 55, (PB) View citations (5)
18. Cloud cover and expected oil returns
    Palgrave Communications, 2023, 10, (1), 1-10 View citations (2)
19. Eye in outer space: satellite imageries of container ports can predict world stock returns
    Palgrave Communications, 2023, 10, (1), 1-16
20. Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index
    Research in International Business and Finance, 2023, 65, (C) View citations (2)
21. Forecasting crude oil futures market returns: A principal component analysis combination approach
    International Journal of Forecasting, 2023, 39, (2), 659-673 View citations (10)
22. Forecasting crude oil market volatility using variable selection and common factor
    International Journal of Forecasting, 2023, 39, (1), 486-502 View citations (14)
23. Forecasting crude oil price returns: Can nonlinearity help?
    Energy, 2023, 262, (PB) View citations (4)
24. Forecasting crude oil prices: A reduced-rank approach
    International Review of Economics & Finance, 2023, 88, (C), 698-711 View citations (7)
25. Forecasting stock market realized volatility: the role of global terrorist attacks
    Applied Economics, 2023, 55, (22), 2551-2566
26. Forecasting stock market volatility: The sum of the parts is more than the whole
    Finance Research Letters, 2023, 55, (PA) View citations (1)
27. Forecasting the real prices of crude oil: What is the role of parameter instability?
    Energy Economics, 2023, 117, (C) View citations (2)
28. Forecasting the stock risk premium: A new statistical constraint
    Journal of Forecasting, 2023, 42, (7), 1805-1822
29. Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility
    International Journal of Forecasting, 2023, 39, (3), 1318-1332 View citations (18)
30. Hedging pressure momentum and the predictability of oil futures returns
    Economic Modelling, 2023, 121, (C) View citations (2)
31. Not all geopolitical shocks are alike: Identifying price dynamics in the crude oil market under tensions
    Resources Policy, 2023, 80, (C) View citations (10)
32. Portfolios with return and volatility prediction for the energy stock market
    Energy, 2023, 270, (C) View citations (1)
33. The predictability of iron ore futures prices: A product‐material lead–lag effect
    Journal of Futures Markets, 2023, 43, (9), 1289-1304
34. The predictive effect of risk aversion on oil returns under different market conditions
    Energy Economics, 2023, 126, (C) View citations (1)

2022

35. Forecasting Bitcoin volatility: A new insight from the threshold regression model
    Journal of Forecasting, 2022, 41, (3), 633-652 View citations (4)
36. Forecasting crude oil market returns: Enhanced moving average technical indicators
    Resources Policy, 2022, 76, (C) View citations (5)
37. Forecasting crude oil market volatility: A newspaper-based predictor regarding petroleum market volatility
    Resources Policy, 2022, 79, (C) View citations (6)
38. Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach
    Journal of Forecasting, 2022, 41, (2), 230-251 View citations (5)
39. Forecasting the Chinese stock market volatility: A regression approach with a t-distributed error
    Applied Economics, 2022, 54, (50), 5811-5826 View citations (4)
40. Forecasting the real prices of crude oil: A robust weighted least squares approach
    Energy Economics, 2022, 116, (C)
41. Geopolitical risk trends and crude oil price predictability
    Energy, 2022, 258, (C) View citations (40)
42. Good oil volatility, bad oil volatility, and stock return predictability
    International Review of Economics & Finance, 2022, 80, (C), 953-966 View citations (8)
43. Good volatility, bad volatility, and time series return predictability
    The European Journal of Finance, 2022, 28, (6), 571-595 View citations (1)
44. Information connectedness of international crude oil futures: Evidence from SC, WTI, and Brent
    International Review of Financial Analysis, 2022, 81, (C) View citations (34)
45. Macroeconomic uncertainty, speculation, and energy futures returns: Evidence from a quantile regression
    Energy, 2022, 241, (C) View citations (15)
46. Managerial ability and idiosyncratic volatility
    International Journal of Finance & Economics, 2022, 27, (2), 2566-2581 View citations (3)
47. Oil implied volatility and expected stock returns along the worldwide supply chain
    Energy Economics, 2022, 114, (C) View citations (4)
48. Shrinking return forecasts
    The Financial Review, 2022, 57, (3), 641-661 View citations (2)
49. To jump or not to jump: momentum of jumps in crude oil price volatility prediction
    Financial Innovation, 2022, 8, (1), 1-31 View citations (2)
50. Uncertainty and the predictability of stock returns
    Journal of Forecasting, 2022, 41, (4), 765-792 View citations (1)

2021

51. Economic-environmental equilibrium-based bi-level dispatch strategy towards integrated electricity and natural gas systems
    Applied Energy, 2021, 281, (C) View citations (15)
52. Forecasting US stock market volatility: How to use international volatility information
    Journal of Forecasting, 2021, 40, (5), 733-768 View citations (13)
53. Forecasting aggregate market volatility: The role of good and bad uncertainties
    Journal of Forecasting, 2021, 40, (1), 40-61 View citations (8)
54. Forecasting crude oil prices: A scaled PCA approach
    Energy Economics, 2021, 97, (C) View citations (57)
55. Forecasting stock returns: A time-dependent weighted least squares approach
    Journal of Financial Markets, 2021, 53, (C) View citations (9)
56. Futures Hedging in CSI 300 Markets: A Comparison Between Minimum-Variance and Maximum-Utility Frameworks
    Computational Economics, 2021, 57, (2), 719-742 View citations (1)
57. How does corporate investment react to oil prices changes? Evidence from China
    Energy Economics, 2021, 97, (C) View citations (12)
58. Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism
    Economic Modelling, 2021, 96, (C), 209-219 View citations (10)
59. Investor attention and oil market volatility: Does economic policy uncertainty matter?
    Energy Economics, 2021, 97, (C) View citations (36)
60. Macroeconomic uncertainty and expected shortfall (and value at risk): a new dynamic semiparametric model
    Quantitative Finance, 2021, 21, (11), 1791-1805 View citations (2)
61. Realized bipower variation, jump components, and option valuation
    Journal of Futures Markets, 2021, 41, (12), 1933-1958 View citations (1)
62. Realized skewness and the short-term predictability for aggregate stock market volatility
    Economic Modelling, 2021, 103, (C) View citations (13)
63. The asymmetric effects of oil price changes on China’s exports: New evidence from a nonlinear autoregressive distributed lag model
    Journal of Asian Economics, 2021, 77, (C)
64. Volatility linkages between stock and commodity markets revisited: Industry perspective and portfolio implications
    Resources Policy, 2021, 74, (C) View citations (7)
65. What can we learn from the return predictability over the business cycle?
    Journal of Forecasting, 2021, 40, (1), 108-131 View citations (6)

2020

66. Can commodity prices forecast exchange rates?
    Energy Economics, 2020, 87, (C) View citations (15)
67. Extreme risk spillovers between crude oil prices and the U.S. exchange rate: Evidence from oil-exporting and oil-importing countries
    Energy, 2020, 212, (C) View citations (28)
68. Forecasting commodity prices out-of-sample: Can technical indicators help?
    International Journal of Forecasting, 2020, 36, (2), 666-683 View citations (29)
69. Forecasting stock returns: A predictor-constrained approach
    Journal of Empirical Finance, 2020, 55, (C), 200-217 View citations (18)
    See also Working Paper Forecasting Stock Returns: A Predictor-Constrained Approach, Working Papers (2018) (2018)
70. Forecasting the real prices of crude oil using robust regression models with regularization constraints
    Energy Economics, 2020, 86, (C) View citations (20)
71. Industry equi-correlation: A powerful predictor of stock returns
    Journal of Empirical Finance, 2020, 59, (C), 1-24 View citations (13)
72. Information transmission between gold and financial assets: Mean, volatility, or risk spillovers?
    Resources Policy, 2020, 69, (C) View citations (16)
73. Macroeconomic fundamentals, jump dynamics and expected volatility
    Quantitative Finance, 2020, 20, (8), 1345-1371 View citations (10)
74. Oil price shocks and Chinese economy revisited: New evidence from SVAR model with sign restrictions
    International Review of Economics & Finance, 2020, 69, (C), 20-32 View citations (17)

2019

75. Dynamic portfolio allocation with time-varying jump risk
    Journal of Empirical Finance, 2019, 50, (C), 113-124 View citations (13)
76. Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?
    Journal of Empirical Finance, 2019, 54, (C), 97-117 View citations (155)
77. Futures hedging in crude oil markets: A comparison between minimum-variance and minimum-risk frameworks
    Energy, 2019, 181, (C), 815-826 View citations (14)
78. Heterogeneous beliefs and aggregate market volatility revisited: New evidence from China
    Pacific-Basin Finance Journal, 2019, 55, (C), 127-141 View citations (8)
79. Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model
    Journal of Futures Markets, 2019, 39, (6), 744-776 View citations (18)
80. It's not that important: The negligible effect of oil market uncertainty
    International Review of Economics & Finance, 2019, 60, (C), 62-84 View citations (3)
81. Oil price increases and the predictability of equity premium
    Journal of Banking & Finance, 2019, 102, (C), 43-58 View citations (92)
82. Risk spillovers between oil and stock markets: A VAR for VaR analysis
    Energy Economics, 2019, 80, (C), 524-535 View citations (60)
83. Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective
    Energy Economics, 2019, 80, (C), 995-1009 View citations (95)

2018

84. Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model
    Energy Economics, 2018, 72, (C), 177-187 View citations (14)
85. Momentum of return predictability
    Journal of Empirical Finance, 2018, 45, (C), 141-156 View citations (28)
86. Oil and the short-term predictability of stock return volatility
    Journal of Empirical Finance, 2018, 47, (C), 90-104 View citations (103)
87. Predictability of crude oil prices: An investor perspective
    Energy Economics, 2018, 75, (C), 193-205 View citations (18)
88. The dynamic spillover between carbon and energy markets: New evidence
    Energy, 2018, 149, (C), 24-33 View citations (116)
89. Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model
    Journal of Forecasting, 2018, 37, (3), 385-400 View citations (25)

2017

90. Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models
    Energy Economics, 2017, 66, (C), 337-348 View citations (58)
91. Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model
    Journal of Empirical Finance, 2017, 43, (C), 130-142 View citations (120)
92. Oil volatility risk and stock market volatility predictability: Evidence from G7 countries
    Energy Economics, 2017, 68, (C), 240-254 View citations (35)
93. Revisiting the multifractality in stock returns and its modeling implications
    Physica A: Statistical Mechanics and its Applications, 2017, 467, (C), 11-20 View citations (13)
94. The effects of oil shocks on export duration of China
    Energy, 2017, 125, (C), 55-61 View citations (5)
95. Time‐Varying Parameter Realized Volatility Models
    Journal of Forecasting, 2017, 36, (5), 566-580 View citations (12)
96. Understanding the multifractality in portfolio excess returns
    Physica A: Statistical Mechanics and its Applications, 2017, 466, (C), 346-355 View citations (7)

2016

97. A nonparametric approach to test for predictability
    Economics Letters, 2016, 148, (C), 10-16 View citations (1)
98. Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging
    Empirical Economics, 2016, 50, (4), 1481-1509 View citations (34)
99. Disentangling the determinants of real oil prices
    Energy Economics, 2016, 56, (C), 363-373 View citations (45)
100. Forecasting crude oil market volatility: A Markov switching multifractal volatility approach
     International Journal of Forecasting, 2016, 32, (1), 1-9 View citations (68)
101. Forecasting realized volatility in a changing world: A dynamic model averaging approach
     Journal of Banking & Finance, 2016, 64, (C), 136-149 View citations (171)
102. Multifractal characterization of energy stocks in China: A multifractal detrended fluctuation analysis
     Physica A: Statistical Mechanics and its Applications, 2016, 451, (C), 357-365 View citations (24)
103. Multifractal detrended cross-correlations between crude oil market and Chinese ten sector stock markets
     Physica A: Statistical Mechanics and its Applications, 2016, 462, (C), 255-265 View citations (28)
104. Oil price shocks and U.S. dollar exchange rates
     Energy, 2016, 112, (C), 1036-1048 View citations (36)
105. The relationships between petroleum and stock returns: An asymmetric dynamic equi-correlation approach
     Energy Economics, 2016, 56, (C), 453-463 View citations (25)
106. What the investors need to know about forecasting oil futures return volatility
     Energy Economics, 2016, 57, (C), 128-139 View citations (15)

2015

107. Commodity price changes and the predictability of economic policy uncertainty
     Economics Letters, 2015, 127, (C), 39-42 View citations (57)
108. Forecasting excess stock returns with crude oil market data
     Energy Economics, 2015, 48, (C), 316-324 View citations (43)
109. Forecasting the real prices of crude oil under economic and statistical constraints
     Energy Economics, 2015, 51, (C), 599-608 View citations (39)
110. Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy?
     Management Science, 2015, 61, (12), 2870-2889 View citations (60)
111. Limited attention of individual investors and stock performance: Evidence from the ChiNext market
     Economic Modelling, 2015, 50, (C), 94-104 View citations (38)

2014

112. Cross-correlations between spot and futures markets of nonferrous metals
     Physica A: Statistical Mechanics and its Applications, 2014, 400, (C), 20-30 View citations (12)
113. Hedging crude oil using refined product: A regime switching asymmetric DCC approach
     Energy Economics, 2014, 46, (C), 472-484 View citations (46)
114. Oil price shocks and agricultural commodity prices
     Energy Economics, 2014, 44, (C), 22-35 View citations (144)

2013

115. Are crude oil spot and futures prices cointegrated? Not always!
     Economic Modelling, 2013, 33, (C), 641-650 View citations (23)
116. Efficiency of Crude Oil Futures Markets: New Evidence from Multifractal Detrending Moving Average Analysis
     Computational Economics, 2013, 42, (4), 393-414 View citations (21)
117. Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries
     Journal of Comparative Economics, 2013, 41, (4), 1220-1239 View citations (364)

2012

118. Energy prices and exchange rates of the U.S. dollar: Further evidence from linear and nonlinear causality analysis
     Economic Modelling, 2012, 29, (6), 2289-2297 View citations (64)
119. Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?
     Energy Economics, 2012, 34, (6), 2167-2181 View citations (112)
120. Long memory in energy futures markets: Further evidence
     Resources Policy, 2012, 37, (3), 261-272 View citations (24)
121. What can we learn from the history of gasoline crack spreads?: Long memory, structural breaks and modeling implications
     Economic Modelling, 2012, 29, (2), 349-360 View citations (8)

2011

122. A copula–multifractal volatility hedging model for CSI 300 index futures
     Physica A: Statistical Mechanics and its Applications, 2011, 390, (23), 4260-4272 View citations (14)
123. Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis
     Physica A: Statistical Mechanics and its Applications, 2011, 390, (5), 817-827 View citations (86)
124. Analysis of the efficiency of the Shanghai stock market: A volatility perspective
     Physica A: Statistical Mechanics and its Applications, 2011, 390, (20), 3486-3495 View citations (12)
125. Can GARCH-class models capture long memory in WTI crude oil markets?
     Economic Modelling, 2011, 28, (3), 921-927 View citations (41)
126. Detrended fluctuation analysis on spot and futures markets of West Texas Intermediate crude oil
     Physica A: Statistical Mechanics and its Applications, 2011, 390, (5), 864-875 View citations (71)
127. Multifractal detrending moving average analysis on the US Dollar exchange rates
     Physica A: Statistical Mechanics and its Applications, 2011, 390, (20), 3512-3523 View citations (56)

2010

128. Analysis of efficiency for Shenzhen stock market: Evidence from the source of multifractality
     International Review of Financial Analysis, 2010, 19, (4), 237-241 View citations (14)
129. Analysis of market efficiency for the Shanghai stock market over time
     Physica A: Statistical Mechanics and its Applications, 2010, 389, (8), 1635-1642 View citations (48)
130. Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective
     Physica A: Statistical Mechanics and its Applications, 2010, 389, (24), 5759-5768 View citations (18)
131. Cross-correlations between Chinese A-share and B-share markets
     Physica A: Statistical Mechanics and its Applications, 2010, 389, (23), 5468-5478 View citations (98)
132. Forecasting crude oil market volatility: Further evidence using GARCH-class models
     Energy Economics, 2010, 32, (6), 1477-1484 View citations (219)
133. Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis
     Energy Economics, 2010, 32, (5), 987-992 View citations (101)
134. Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis
     Physica A: Statistical Mechanics and its Applications, 2010, 389, (14), 2805-2815 View citations (61)

2009

135. Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis
     International Review of Financial Analysis, 2009, 18, (5), 271-276 View citations (121)