1. Investor clientele and intraday patterns in the cross section of stock returns. (2025). Mahmud, Syed ; Khan, Ali ; Haboub, Ahmad ; Chen, Jian.
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   RePEc:kap:rqfnac:v:64:y:2025:i:2:d:10.1007_s11156-024-01319-8.

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2. The “night effect” of intraday trading: Evidence from Chinese gold and silver futures markets. (2025). Zhou, Ivy Z ; Xu, Yahua ; Bouri, Elie ; Ma, Gaoping.
   In: Global Finance Journal.
   RePEc:eee:glofin:v:64:y:2025:i:c:s1044028325000110.

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3. The nexus of overnight trend and asset prices in China. (2025). Li, Youwei ; Guo, Jiaqi ; Han, Xing.
   In: Journal of Economic Dynamics and Control.
   RePEc:eee:dyncon:v:170:y:2025:i:c:s0165188924001891.

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4. Pattern Recognition in Microtrading Behaviors Preceding Stock Price Jumps: A Study Based on Mutual Information for Multivariate Time Series. (2024). Li, Xindan ; Azencott, Robert ; Zhu, Hongliang ; Kong, AO.
   In: Computational Economics.
   RePEc:kap:compec:v:63:y:2024:i:4:d:10.1007_s10614-023-10367-6.

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5. Is there an intraday reversal effect in commodity futures and options? Evidence from the Chinese market. (2024). Zheng, Luyuan ; Luo, Xingguo.
   In: Pacific-Basin Finance Journal.
   RePEc:eee:pacfin:v:88:y:2024:i:c:s0927538x24002865.

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6. Asymmetric trading restriction and return comovement. (2024). Yang, Lihua ; Zhu, Hongbing ; Zhang, Bing.
   In: Journal of International Financial Markets, Institutions and Money.
   RePEc:eee:intfin:v:94:y:2024:i:c:s1042443124000891.

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7. Under the microscope: Trade initiation activities around earnings and takeover announcements in a market with continuous disclosure. (2024). Kalev, Petko S ; Mudalige, Priyantha.
   In: Global Finance Journal.
   RePEc:eee:glofin:v:63:y:2024:i:c:s1044028324001261.

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8. Overnight versus intraday returns of anomalies in China. (2023). Chou, Robin K ; Chang, Hui-Wen ; Lin, Chaonan.
   In: Pacific-Basin Finance Journal.
   RePEc:eee:pacfin:v:79:y:2023:i:c:s0927538x23000732.

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9. Does short-term momentum exist in China?. (2023). Ruan, Xinfeng ; Yue, Tian ; Li, Tianjiao.
   In: Pacific-Basin Finance Journal.
   RePEc:eee:pacfin:v:77:y:2023:i:c:s0927538x22002153.

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10. Which is more important in stock market forecasting: Attention or sentiment?. (2023). Li, Yishuo ; Zhang, Xiaotao ; Wu, Ji George ; Zou, Gaofeng.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:89:y:2023:i:c:s105752192300248x.

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11. From dusk till dawn (and vice versa): Overnight-versus-daytime reversals and feedback trading. (2023). Kallinterakis, Vasileios ; Karaa, Rabaa.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:85:y:2023:i:c:s1057521922003933.

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12. Intraday and overnight tail risks and return predictability in the crude oil market: Evidence from oil-related regular news and extreme shocks. (2023). Xu, Yahua ; Bouri, Elie ; Zhang, Dingsheng ; Wang, Cheng.
    In: Energy Economics.
    RePEc:eee:eneeco:v:127:y:2023:i:pb:s0140988323006199.

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13. The relative importance of overnight sentiment versus trading-hour sentiment in volatility forecasting. (2023). Wan, Xinmin ; Chu, Xiaojun ; Qiu, Jianying.
    In: Journal of Behavioral and Experimental Finance.
    RePEc:eee:beexfi:v:39:y:2023:i:c:s2214635023000400.

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14. The Asymmetric Overnight Return Anomaly in the Chinese Stock Market. (2022). Li, Youwei ; An, Yahui ; Huang, Lin.
    In: JRFM.
    RePEc:gam:jjrfmx:v:15:y:2022:i:11:p:534-:d:974482.

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15. Overnight returns, daytime reversals, and future stock returns: Is China different?. (2022). Chiah, Mardy ; Cheema, Muhammad ; Man, Yimei.
    In: Pacific-Basin Finance Journal.
    RePEc:eee:pacfin:v:74:y:2022:i:c:s0927538x22001044.

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16. The effects of overnight events on daytime trading sessions. (2022). Webb, Robert I ; Ham, Hyuna ; Ryu, Doojin.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:83:y:2022:i:c:s1057521922001892.

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17. Oil price uncertainty and stock price informativeness: Evidence from listed U.S. companies. (2022). Huang, Yuxuan ; Jin, Sisi ; Zhu, QI ; Yan, Cheng.
    In: Energy Economics.
    RePEc:eee:eneeco:v:113:y:2022:i:c:s0140988322003474.

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18. Intraday return predictability in the cryptocurrency markets: Momentum, reversal, or both. (2022). Wen, Zhuzhu ; Zhao, Yang ; Xu, Yahua ; Bouri, Elie.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:62:y:2022:i:c:s1062940822000833.

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19. Global momentum: The optimal trading approach. (2022). Muradoglu, Yaz ; Muradolu, Yaz Gulnur ; Tsitsianis, Nicholas ; Wouassom, Alain.
    In: Journal of Behavioral and Experimental Finance.
    RePEc:eee:beexfi:v:36:y:2022:i:c:s2214635022000788.

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20. Investor heterogeneity and momentum-based trading strategies in China. (2021). Li, Youwei ; Gao, YA ; Xiong, Xiong ; Han, Xing.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:74:y:2021:i:c:s1057521920302957.

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21. Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism. (2021). Zhang, Yaojie ; Wang, Yudong ; Wen, Danyan.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:96:y:2021:i:c:p:209-219.

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22. Intraday time-series momentum and investor trading behavior. (2021). Onishchenko, Olena ; Zhao, Jing ; Roberts, Helen ; Kuruppuarachchi, Duminda.
    In: Journal of Behavioral and Experimental Finance.
    RePEc:eee:beexfi:v:31:y:2021:i:c:s2214635021001015.

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23. Bond intraday momentum. (2021). Li, YI ; Zhang, Wei ; Wang, Pengfei.
    In: Journal of Behavioral and Experimental Finance.
    RePEc:eee:beexfi:v:31:y:2021:i:c:s2214635021000599.

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24. Intraday time‐series momentum: Evidence from China. (2020). Li, Youwei ; Kearney, Fearghal ; Yang, Yung Chiang ; Jin, Muzhao.
    In: Journal of Futures Markets.
    RePEc:wly:jfutmk:v:40:y:2020:i:4:p:632-650.

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25. Intraday Time-series Momentum: Evidence from China. (2019). Li, Youwei ; Kearney, Fearghal ; Yang, Yung Chiang ; Jin, Muzhao.
    In: MPRA Paper.
    RePEc:pra:mprapa:97134.

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1. Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations. (2017). Liu, Zhi.
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2. The contribution of jumps to forecasting the density of returns. (2017). Sévi, Benoît ; Ielpo, Florian ; Chorro, Christophe ; Sevi, Benoit.
   In: Documents de travail du Centre d'Economie de la Sorbonne.
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3. The contribution of jumps to forecasting the density of returns. (2017). Sévi, Benoît ; Ielpo, Florian ; Sevi, Benoit ; Chorro, Christophe.
   In: Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers).
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4. Information Shocks and Short-Term Market Underreaction. (2017). Zhu, Kevin X ; Jiang, George J.
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5. Modeling spot rate using a realized stochastic volatility model with level effect and dynamic drift☆. (2017). Zheng, Tingguo ; Li, Shaoyu.
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6. High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed. (2017). Lilla, F.
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7. Do co-jumps impact correlations in currency markets?. (2017). Vacha, Lukas ; Baruník, Jozef ; Barunik, Jozef.
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8. Modeling Jumps and Volatility of the Indian Stock Market Using High-Frequency Data. (2016). Sen, Rituparna ; Mehrotra, Pulkit .
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9. Forecasting oil price realized volatility: A new approach. (2016). Filis, George ; Degiannakis, Stavros.
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10. Volatility Forecasting: Downside Risk, Jumps and Leverage Effect. (2016). Hu, Yujia ; Audrino, Francesco.
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11. Intra-day realized volatility for European and USA stock indices. (2016). Floros, Christos ; Degiannakis, Stavros.
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12. A compound duration model for high-frequency asset returns. (2016). Aldrich, Eric ; Heckenbach, Indra ; Laughlin, Gregory.
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13. Jumps in equilibrium prices and asymmetric news in foreign exchange markets. (2016). Uctum, Remzi ; El Ouadghiri, Imane.
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14. Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach. (2016). Palm, Franz ; LECOURT, Christelle ; Laurent, Sébastien.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:100:y:2016:i:c:p:383-400.

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15. Media-expressed negative tone and firm-level stock returns. (2016). Hutson, Elaine ; Liu, Sha ; Ahmad, Khurshid ; Han, Jingguang ; Kearney, Colm.
    In: Journal of Corporate Finance.
    RePEc:eee:corfin:v:37:y:2016:i:c:p:152-172.

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16. High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models. (2016). Lilla, F.
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17. Intra-Day Realized Volatility for European and USA Stock Indices. (2015). Floros, Christos ; Degiannakis, Stavros.
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18. Volatility Forecast in Crises and Expansions. (2015). Pypko, Sergii.
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19. Market risk of BRIC Eurobonds in the financial crisis period. (2015). VORTELINOS, DIMITRIOS ; Lakshmi, Geeta.
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20. Intraday jumps in Chinas Treasury bond market and macro news announcements. (2015). Cui, Jing ; Zhao, Hua.
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21. Information transmission between stock markets in Hong Kong, Europe and the US: New evidence on time- and state-dependence. (2015). Maderitsch, R.
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22. A variance spillover analysis without covariances: What do we miss?. (2015). Fengler, Matthias ; Gisler, Katja I. M., .
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23. Testing the mixture of distributions hypothesis on target stocks. (2015). Carroll, Rachael ; Kearney, Colm.
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24. Collective synchronization and high frequency systemic instabilities in financial markets. (2015). Marmi, Stefano ; Treccani, Michele ; Calcagnile, Lucio Maria ; Bormetti, Giacomo ; Lillo, Fabrizio.
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25. The Risk Return Relationship: Evidence from Index Return and Realised Variance Series. (2014). Yang, Minxian.
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26. Currency jumps and crises: Do developed and emerging market currencies jump together?. (2014). Treepongkaruna, Sirimon ; Chan, Kam Fong ; Powell, John G..
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27. Currency jumps, cojumps and the role of macro news. (2014). Miao, Hong ; Villupuram, Sriram ; Ramchander, Sanjay ; Chatrath, Arjun.
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28. Structural breaks in volatility spillovers between international financial markets: Contagion or mere interdependence?. (2014). Jung, Robert ; Maderitsch, R..
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29. Cojumps in stock prices: Empirical evidence. (2014). Shackleton, Mark ; Taylor, Stephen J. ; Gilder, Dudley.
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30. Analysis of Spin Financial Market by GARCH Model. (2014). Takaishi, Tetsuya.
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31. Price Jump Indicators: Stock Market Empirics During the Crisis. (2013). Novotny, Jan ; Kočenda, Evžen ; Hanousek, Jan ; Koenda, Even.
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32. Additive modeling of realized variance: tests for parametric specifications and structural breaks. (2013). Fengler, Matthias ; Vogt, Michael ; Mammen, Enno.
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33. Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?. (2013). Malec, Peter ; Hautsch, Nikolaus ; Kyj, Lada M..
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34. Disentangling the effect of jumps on systematic risk using a new estimator of integrated co-volatility. (2013). Liu, Zhi ; Wang, Kent.
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35. Financial Risk Measurement for Financial Risk Management. (2013). Andersen, Torben G ; Bollerslev, Tim ; Christoffersen, Peter F ; Diebold, Francis X.
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36. Jump tails, extreme dependencies, and the distribution of stock returns. (2013). Bollerslev, Tim ; Todorov, Viktor ; Li, Sophia Zhengzi.
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37. Jumps in Oil Prices: The Role of Economic News. (2013). Miao, Hong ; Elder, John ; Ramchander, Sanjay ; John Elder, Hong Miao,, .
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38. On the forecasting accuracy of multivariate GARCH models. (2012). Violante, Francesco ; Laurent, Sébastien ; Jeroen V. K. Rombouts, .
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39. Cojumping: Evidence from the US Treasury bond and futures markets. (2012). Dungey, Mardi ; Hvozdyk, Lyudmyla.
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41. On the volatility–volume relationship in energy futures markets using intraday data. (2012). Sévi, Benoît ; Chevallier, Julien.
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45. The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures. (2011). Koopman, Siem Jan ; Scharth, Marcel.
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47. Brownian motion vs. pure-jump processes for individual stocks. (2011). Sévi, Benoît ; Baena, Csar ; Svi, Benot.
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48. Cojumping: Evidence from the US Treasury Bond and Future Markets (Discussion Paper 2010-06). (2010). .
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49. The properties of realized correlation: Evidence from the French, German and Greek equity markets. (2010). VORTELINOS, DIMITRIOS.
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50. .

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