1. Did real economic uncertainty drive risk connectedness in the oil–stock nexus during the COVID-19 outbreak? A partial wavelet coherence analysis. (2023). Maghyereh, Aktham ; Al-Shboul, Mohammad.
   In: Journal of Economic Structures.
   RePEc:spr:jecstr:v:12:y:2023:i:1:d:10.1186_s40008-023-00306-x.

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2. Chinas energy stock market jumps: To what extent does the COVID-19 pandemic play a part?. (2022). Wang, Qunwei ; Bi, Xiaoyi ; Dai, Xingyu ; Tong, Yuan.
   In: Energy Economics.
   RePEc:eee:eneeco:v:109:y:2022:i:c:s0140988322001153.

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3. The realized volatility of commodity futures: Interconnectedness and determinants#. (2021). Vo, Xuan Vinh ; Saeed, Tareq ; Lucey, Brian ; Bouri, Elie.
   In: International Review of Economics & Finance.
   RePEc:eee:reveco:v:73:y:2021:i:c:p:139-151.

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4. Stock market volatility forecasting: Do we need high-frequency data?. (2021). Molnár, Peter ; Lyócsa, Štefan ; Vrost, Toma ; Molnar, Peter ; Lyocsa, Tefan.
   In: International Journal of Forecasting.
   RePEc:eee:intfor:v:37:y:2021:i:3:p:1092-1110.

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5. Modelling the volatility of TOCOM energy futures: A regime switching realised volatility approach. (2021). Marsh, Ian W ; Huang, Chih-Yueh ; Alizadeh, Amir H.
   In: Energy Economics.
   RePEc:eee:eneeco:v:93:y:2021:i:c:s0140988319302063.

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6. Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices. (2020). Fang, Yongmei ; Heravi, Saeed ; Wu, Shangjuan ; Guan, BO.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:39:y:2020:i:6:p:877-886.

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7. An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment. (2020). Latif, Khalid ; Afzal, Sitara ; Yasir, Muhammad ; Song, Oh-Young ; Shahzad, Farhan ; Malik, Nazish Yameen ; Chaudhary, Ghulam Mujtaba.
   In: Sustainability.
   RePEc:gam:jsusta:v:12:y:2020:i:4:p:1660-:d:324085.

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8. Forecasting oil price volatility using high-frequency data: New evidence. (2020). Liu, Jing ; Wei, YU ; Ma, Feng ; Chen, Wang.
   In: International Review of Economics & Finance.
   RePEc:eee:reveco:v:66:y:2020:i:c:p:1-12.

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9. Financial factors affecting oil price change and oil-stock interactions: a review and future perspectives. (2019). Lv, Tao ; Ding, Zhihua ; Liu, Zhenhua ; Qiang, Wei ; Wu, Jy S.
   In: Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards.
   RePEc:spr:nathaz:v:95:y:2019:i:1:d:10.1007_s11069-018-3473-y.

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10. Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach. (2019). Wen, Fenghua ; Li, Steven ; Hou, Yang.
    In: Energy Economics.
    RePEc:eee:eneeco:v:83:y:2019:i:c:p:119-143.

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11. Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets. (2019). Ma, Feng ; Chen, Yixiang ; Zhang, Yaojie.
    In: Energy Economics.
    RePEc:eee:eneeco:v:81:y:2019:i:c:p:52-62.

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12. Forecasting the volatility of crude oil futures using high-frequency data: further evidence. (2018). Ma, Feng ; He, Feng ; Chen, Wang ; Wei, YU.
    In: Empirical Economics.
    RePEc:spr:empeco:v:55:y:2018:i:2:d:10.1007_s00181-017-1294-6.

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13. Intraday realised volatility forecasting and announcements. (2018). Vortelinos, Dimitrios I.
    In: International Journal of Banking, Accounting and Finance.
    RePEc:ids:injbaf:v:9:y:2018:i:1:p:88-118.

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14. Modeling and forecasting multifractal volatility established upon the heterogeneous market hypothesis. (2018). Tao, Qizhi ; Zhang, Ting ; Liu, Jiapeng ; Wei, YU.
    In: International Review of Economics & Finance.
    RePEc:eee:reveco:v:54:y:2018:i:c:p:143-153.

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15. Does US Economic Policy Uncertainty matter for European stock markets volatility?. (2018). Mei, Dexiang ; Hou, Wenjing ; Zhang, Yaojie ; Zeng, Qing.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:512:y:2018:i:c:p:215-221.

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16. Volatility forecasting: Global economic policy uncertainty and regime switching. (2018). Yu, Miao ; Song, Jinguo.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:511:y:2018:i:c:p:316-323.

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17. Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?. (2018). Peng, Huan ; Diao, Xiaohua ; Mei, Dexiang ; Chen, Ruoxun.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:501:y:2018:i:c:p:78-85.

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18. Leverage effect, economic policy uncertainty and realized volatility with regime switching. (2018). Duan, Yinying ; Liu, Zhicao ; Zeng, Qing ; Chen, Wang.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:493:y:2018:i:c:p:148-154.

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19. Does the OVX matter for volatility forecasting? Evidence from the crude oil market. (2018). Lv, Wendai.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:492:y:2018:i:c:p:916-922.

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20. Are low-frequency data really uninformative? A forecasting combination perspective. (2018). Ma, Feng ; Zhang, Yaojie ; Liu, LI.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:44:y:2018:i:c:p:92-108.

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21. Fuel Consumption in Ukraine: Evidence from Vector Error Correction Model. (2018). Oliskevych, Marianna ; Tokarchuk, Viktor ; Beregova, Galyna.
    In: International Journal of Energy Economics and Policy.
    RePEc:eco:journ2:2018-05-8.

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22. Modeling and predicting historical volatility in exchange rate markets. (2017). Lahmiri, Salim.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:471:y:2017:i:c:p:387-395.

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23. A study on chaos in crude oil markets before and after 2008 international financial crisis. (2017). Lahmiri, Salim.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:466:y:2017:i:c:p:389-395.

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24. Forecasting the realized volatility in the Chinese stock market: further evidence. (2016). Pu, Wang ; Ma, Feng ; Chen, Yixiang.
    In: Applied Economics.
    RePEc:taf:applec:v:48:y:2016:i:33:p:3116-3130.

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25. Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques. (2016). Lahmiri, Salim.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:444:y:2016:i:c:p:388-396.

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26. High and low or close to close prices? Evidence from the multifractal volatility. (2015). Ma, Feng ; Liu, Zhichao ; Long, Yujia .
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:427:y:2015:i:c:p:50-61.

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27. Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility. (2014). Huang, Dengshi ; Wei, YU ; Chen, Yixiang ; Ma, Feng.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:405:y:2014:i:c:p:171-180.

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28. Does long memory matter in forecasting oil price volatility?. (2013). Naderi, Esmaeil ; gandali alikhani, nadiya ; Delavari, Majid .
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    RePEc:pra:mprapa:46356.

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29. The Effect of Crude Oil Price on the Methanol price. (2012). gandali alikhani, nadiya ; Delavari, Majid .
    In: MPRA Paper.
    RePEc:pra:mprapa:49727.

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18. Features and evolution of international crude oil trade relationships: A trading-based network analysis. (2014). An, Haizhong ; Chen, Yurong ; Gao, Xiangyun ; Zhong, Weiqiong ; Li, Huajiao.
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23. Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?. (2012). Wei, YU.
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24. A Monte Carlo simulation to the performance of the R/S and V/S methods—Statistical revisit and real world application. (2012). He, Ling-Yun ; Qian, Wen-Bin .
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25. Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?. (2012). Wang, Yudong ; Wu, Chongfeng.
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26. Energy prices and exchange rates of the U.S. dollar: Further evidence from linear and nonlinear causality analysis. (2012). Wang, Yudong ; Wu, Chongfeng.
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27. Chaotic Time Series Analysis in Economics: Balance and Perspectives. (2011). Faggini, Marisa.
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28. Is there co-movement of agricultural commodities futures prices and crude oil?. (2011). McKenzie, Andrew M. ; Natanelov, Valeri ; Alam, Mohammad J. ; van Huylenbroeck, Guido.
    In: Energy Policy.
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29. Can GARCH-class models capture long memory in WTI crude oil markets?. (2011). Wang, Yudong ; Wu, Chongfeng ; Wei, YU.
    In: Economic Modelling.
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30. Exchange Rates and Inflation Rates: Exploring Nonlinear Relationships. (2011). Adrangi, Bahram ; Allender, Mary E. ; Raffiee, Kambiz .
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31. Is There Co-Movement of Agricultural Commodities Futures Prices and Crude Oil?. (2011). McKenzie, Andrew M. ; Natanelov, Valeri ; Alam, Mohammad J. ; van Huylenbroeck, Guido.
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32. Is Price Behavior Scaling and Multiscaling in a Dealer Market? Perspectives from Multi-Agent Based Experiments. (2010). He, Ling-Yun.
    In: Computational Economics.
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33. Wavelet domain correlation between the futures prices of natural gas and oil. (2010). Li, H. C. ; Tonn, Victor Lux ; McCarthy, Joseph .
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34. Multifractal spectrum analysis of nonlinear dynamical mechanisms in China’s agricultural futures markets. (2010). He, Ling-Yun ; Chen, Shu-Peng .
    In: Physica A: Statistical Mechanics and its Applications.
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35. Are developed and emerging agricultural futures markets multifractal? A comparative perspective. (2010). He, Ling-Yun ; Chen, Shu-Peng .
    In: Physica A: Statistical Mechanics and its Applications.
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36. Are crude oil markets multifractal? Evidence from MF-DFA and MF-SSA perspectives. (2010). He, Ling-Yun ; Chen, Shu-Peng .
    In: Physica A: Statistical Mechanics and its Applications.
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37. Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis. (2010). Wang, Yudong ; Chen, Hongtao ; Gu, Rongbao .
    In: Physica A: Statistical Mechanics and its Applications.
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38. Forecasting crude oil market volatility: Further evidence using GARCH-class models. (2010). Wang, Yudong ; Huang, Dengshi ; Wei, YU.
    In: Energy Economics.
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39. A novel algorithm for prediction of crude oil price variation based on soft computing. (2009). Ghaffari, Ali ; Zare, Samaneh .
    In: Energy Economics.
    RePEc:eee:eneeco:v:31:y:2009:i:4:p:531-536.

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40. Impact of speculators expectations of returns and time scales of investment on crude oil price behaviors. (2009). Wei, Yi-Ming ; He, Ling-Yun ; Fan, Ying.
    In: Energy Economics.
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41. Forecasting volatility of crude oil markets. (2009). Yoon, Seong-Min ; Kang, Sang-Mok.
    In: Energy Economics.
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42. Short-term predictability of crude oil markets: A detrended fluctuation analysis approach. (2008). Rodriguez, Eduardo ; Alvarez-Ramirez, Jose .
    In: Energy Economics.
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43. Can the dynamics of the term structure of petroleum futures be forecasted? Evidence from major markets. (2008). Skiadopoulos, George ; Chantziara, Thalia.
    In: Energy Economics.
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44. A generalized pattern matching approach for multi-step prediction of crude oil price. (2008). Wei, Yi-Ming ; Liang, Qiang ; Fan, Ying.
    In: Energy Economics.
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45. Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility. (2007). Tabak, Benjamin ; Cajueiro, Daniel.
    In: Energy Economics.
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46. Modeling dynamic conditional correlations in WTI oil forward and futures returns. (2006). McAleer, Michael ; Manera, Matteo ; Lanza, Alessandro.
    In: Finance Research Letters.
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47. Empirical asset return distributions: is chaos the culprit?. (2004). Muckley, Cal.
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48. Oil Price Volatility and Stock Price Fluctuations in an Emerging Market: Evidence from South Korea. (2003). Rumi, MASIH ; Sanjay, PETERS .
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49. Maturity Effects in Futures Markets: Evidence from Eleven Financial Futures Markets. (2003). Akin, Rita Madarassy .
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50. Maturity Effects in Futures Markets: Evidence from Eleven Financial Futures Markets. (2003). Akin, Rita Madarassy .
    In: Santa Cruz Center for International Economics, Working Paper Series.
    RePEc:cdl:scciec:qt1n04g31b.

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