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, Chinahttp://www.suibe.edu.cn/finance/
RePEc:edi:sfsuicn (more details at EDIRC)
(50%) Shanghai University of International Business and Economics
Shanghai, Chinahttp://www.suibe.edu.cn/
RePEc:edi:shuibcn (more details at EDIRC)
Research output
Jump to: Working papers ArticlesWorking papers
- Qiu, Yue & Xie, Tian & Yu, Jun, 2020. "Forecast combinations in machine learning," Economics and Statistics Working Papers 13-2020, Singapore Management University, School of Economics.
- 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.
Articles
- Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
- 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.
- 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.
- Qiu, Yue, 2021. "Complete subset least squares support vector regression," Economics Letters, Elsevier, vol. 200(C).
- 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.
- Qiu, Yue & Wang, Yifan & Xie, Tian, 2021. "Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies," Economics Letters, Elsevier, vol. 208(C).
- Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
- 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.
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
- 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:
- 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
- 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:
- 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).
- 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.
- 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.
See citations under working paper version above.
- 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.
- 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:
- 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).
- Akbari, Amir & Carrieri, Francesca, 2023. "Global risk and market conditions," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 51-70.
- Qiu, Yue, 2021.
"Complete subset least squares support vector regression,"
Economics Letters, Elsevier, vol. 200(C).
Cited by:
- 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).
- 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.
- 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:
- 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).
- 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).
- 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).
- 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).
- 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.
- 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.
- 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).
- 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.
- 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.
- Qiu, Yue & Wang, Yifan & Xie, Tian, 2021. "Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies," Economics Letters, Elsevier, vol. 208(C).
- 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:
- 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).
- 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).
- 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).
- Li, Shi, 2022. "Spillovers between Bitcoin and Meme stocks," Finance Research Letters, Elsevier, vol. 50(C).
- 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.
- Qiu, Yue, 2020.
"Forecasting the Consumer Confidence Index with tree-based MIDAS regressions,"
Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
Cited by:
- 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.
- 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.
- Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
- 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).
- 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.
- 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:
- 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
Access and download statistics for all items
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.- NEP-BIG: Big Data (2) 2019-03-25 2020-06-15. Author is listed
- NEP-ECM: Econometrics (2) 2019-03-25 2020-06-15. Author is listed
- NEP-FOR: Forecasting (2) 2019-03-25 2020-06-15. Author is listed
- NEP-SEA: South East Asia (2) 2019-03-25 2020-06-15. Author is listed
- NEP-CMP: Computational Economics (1) 2020-06-15. Author is listed
- NEP-ORE: Operations Research (1) 2020-06-15. Author is listed
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