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. 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.
2. 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.

3. 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. 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).
   2. 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.
   3. 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.
   4. 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).
   5. 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).
   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. 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).
   8. 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.
   9. Qiu, Yue & Wang, Yifan & Xie, Tian, 2021. "Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies," Economics Letters, Elsevier, vol. 208(C).

4. 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. Li, Shi, 2022. "Spillovers between Bitcoin and Meme stocks," Finance Research Letters, Elsevier, vol. 50(C).
   2. 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.
   3. 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).
   4. 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).

5. 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. 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.
   2. 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).
   3. 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.
   4. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).

6. 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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