Yue Qiu

Personal Details

First Name:	Yue
Middle Name:
Last Name:	Qiu
Suffix:
RePEc Short-ID:	pqi115
	[This author has chosen not to make the email address public]
Terminal Degree:	2017 (from RePEc Genealogy)

Affiliation

(50%) School of Finance
Shanghai University of International Business and Economics

Shanghai, China
http://www.suibe.edu.cn/finance/
RePEc:edi:sfsuicn (more details at EDIRC)

(50%) Shanghai University of International Business and Economics

Shanghai, China
http://www.suibe.edu.cn/
RePEc:edi:shuibcn (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Qiu, Yue & Xie, Tian & Yu, Jun & Zhou, Qiankun, 2019. "Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks," Economics and Statistics Working Papers 7-2019, Singapore Management University, School of Economics.
   • Yue Qiu & Tian Xie & Jun Yu & Qiankun Zhou, 2022. "Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks [Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts]," Journal of Financial Econometrics, Oxford University Press, vol. 20(1), pages 160-186.

   Cited by:

   1. Chao Liang & Yongan Xu & Zhonglu Chen & Xiafei Li, 2023. "Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3689-3699, October.

Articles

1. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).

   Cited by:

   1. Marco Delogu & Raffaelle Lagravinese & Dimitri Paolini & Giuliano Resce, 2020. "Predicting dropout from higher education: Evidence from Italy," DEM Discussion Paper Series 22-06, Department of Economics at the University of Luxembourg.
      • Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).

2. Yue Qiu & Tian Xie & Jun Yu & Qiankun Zhou, 2022. "Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks [Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts]," Journal of Financial Econometrics, Oxford University Press, vol. 20(1), pages 160-186.
   • Qiu, Yue & Xie, Tian & Yu, Jun & Zhou, Qiankun, 2019. "Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks," Economics and Statistics Working Papers 7-2019, Singapore Management University, School of Economics.
   See citations under working paper version above.
3. Qiu, Yue & Ren, Yu & Xie, Tian, 2022. "Global factors and stock market integration," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 526-551.

   Cited by:

   1. Chang, Kuang-Liang, 2023. "The low-magnitude and high-magnitude asymmetries in tail dependence structures in international equity markets and the role of bilateral exchange rate," Journal of International Money and Finance, Elsevier, vol. 133(C).
   2. Akbari, Amir & Carrieri, Francesca, 2023. "Global risk and market conditions," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 51-70.

4. Qiu, Yue & Wang, Zongrun & Xie, Tian & Zhang, Xinyu, 2021. "Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 179-201.

   Cited by:

   1. Doan, Bao & Jayasuriya, Dulani & Lee, John B. & Reeves, Jonathan J., 2024. "Cryptocurrency systematic risk dynamics," Economics Letters, Elsevier, vol. 241(C).
   2. Zhao, Yihang & Zhou, Zhenxi & Zhang, Kaiwen & Huo, Yaotong & Sun, Dong & Zhao, Huiru & Sun, Jingqi & Guo, Sen, 2023. "Research on spillover effect between carbon market and electricity market: Evidence from Northern Europe," Energy, Elsevier, vol. 263(PF).
   3. Pham, Son Duy & Nguyen, Thao Thac Thanh & Li, Xiao-Ming, 2024. "Stabilizing global foreign exchange markets in the time of COVID-19: The role of vaccinations," Global Finance Journal, Elsevier, vol. 59(C).
   4. Chen, Meichen & Qin, Cong & Zhang, Xiaoyu, 2022. "Cryptocurrency price discrepancies under uncertainty: Evidence from COVID-19 and lockdown nexus," Journal of International Money and Finance, Elsevier, vol. 124(C).
   5. Tapia, Sebastian & Kristjanpoller, Werner, 2022. "Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
   6. Jiqian Wang & Feng Ma & Elie Bouri & Yangli Guo, 2023. "Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 970-988, July.
   7. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
   8. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
   9. Maria Chiara Pocelli & Manuel L. Esquível & Nadezhda P. Krasii, 2023. "Spectral Analysis for Comparing Bitcoin to Currencies and Assets," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
   10. Sohail Ahmad Javeed & Rashid Latief & Umair Akram, 2023. "The Effects of Board Capital on Green Innovation to Improve Green Total Factor Productivity," Sustainability, MDPI, vol. 15(13), pages 1-18, June.
   11. Qiu, Yue & Wang, Yifan & Xie, Tian, 2021. "Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies," Economics Letters, Elsevier, vol. 208(C).

5. Qiu, Yue, 2021. "Complete subset least squares support vector regression," Economics Letters, Elsevier, vol. 200(C).

   Cited by:

   1. Gunnarsson, Elias Søvik & Isern, Håkon Ramon & Kaloudis, Aristidis & Risstad, Morten & Vigdel, Benjamin & Westgaard, Sjur, 2024. "Prediction of realized volatility and implied volatility indices using AI and machine learning: A review," International Review of Financial Analysis, Elsevier, vol. 93(C).
   2. Francesco Audrino & Jonathan Chassot, 2024. "HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning," Papers 2406.08041, arXiv.org.

6. Qiu, Yue & Wang, Yifan & Xie, Tian, 2021. "Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies," Economics Letters, Elsevier, vol. 208(C).

   Cited by:

   1. Yi, Yongsheng & He, Mengxi & Zhang, Yaojie, 2022. "Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
   2. Doan, Bao & Jayasuriya, Dulani & Lee, John B. & Reeves, Jonathan J., 2024. "Cryptocurrency systematic risk dynamics," Economics Letters, Elsevier, vol. 241(C).
   3. Zhao, Yihang & Zhou, Zhenxi & Zhang, Kaiwen & Huo, Yaotong & Sun, Dong & Zhao, Huiru & Sun, Jingqi & Guo, Sen, 2023. "Research on spillover effect between carbon market and electricity market: Evidence from Northern Europe," Energy, Elsevier, vol. 263(PF).
   4. He, Mengxi & Shen, Lihua & Zhang, Yaojie & Zhang, Yi, 2023. "Predicting cryptocurrency returns for real-world investments: A daily updated and accessible predictor," Finance Research Letters, Elsevier, vol. 58(PA).
   5. Li, Shi, 2022. "Spillovers between Bitcoin and Meme stocks," Finance Research Letters, Elsevier, vol. 50(C).
   6. Wu, Lan & Xu, Weiju & Huang, Dengshi & Li, Pan, 2022. "Does the volatility spillover effect matter in oil price volatility predictability? Evidence from high-frequency data," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 299-306.

7. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.

   Cited by:

   1. Crispino, Marta & Loberto, Michele, 2024. "Do people pay attention to climate change? Evidence from Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 219(C), pages 434-449.
   2. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
      • Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
   3. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
   4. Zhao, Shangwei & Xie, Tian & Ai, Xin & Yang, Guangren & Zhang, Xinyu, 2023. "Correcting sample selection bias with model averaging for consumer demand forecasting," Economic Modelling, Elsevier, vol. 123(C).
   5. Huijian Han & Zhiming Li & Zongwei Li, 2023. "Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data," Sustainability, MDPI, vol. 15(4), pages 1-12, February.

8. Yue Qiu & Yu Ren & Tian Xie, 2019. "Weighing asset pricing factors: a least squares model averaging approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(10), pages 1673-1687, October.

   Cited by:

   1. Qiu, Yue & Wang, Zongrun & Xie, Tian & Zhang, Xinyu, 2021. "Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 179-201.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-BIG: Big Data (2) 2019-03-25 2020-06-15. Author is listed
2. NEP-ECM: Econometrics (2) 2019-03-25 2020-06-15. Author is listed
3. NEP-FOR: Forecasting (2) 2019-03-25 2020-06-15. Author is listed
4. NEP-SEA: South East Asia (2) 2019-03-25 2020-06-15. Author is listed
5. NEP-CMP: Computational Economics (1) 2020-06-15. Author is listed
6. NEP-ORE: Operations Research (1) 2020-06-15. Author is listed

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