Yuzhi Cai

Personal Details

First Name:	Yuzhi
Middle Name:
Last Name:	Cai
Suffix:
RePEc Short-ID:	pca1281
	[This author has chosen not to make the email address public]
	http://www.swansea.ac.uk/staff/som/academic-staff/y.cai/

Affiliation

School of Management
Swansea University

Swansea, United Kingdom
http://www.swan.ac.uk/som/
RePEc:edi:bmswauk (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. Yuzhi Cai & Julian Stander, 2018. "The threshold GARCH model: estimation and density forecasting for financial returns," Working Papers 2018-23, Swansea University, School of Management.
   • Yuzhi Cai & Julian Stander, 2020. "The Threshold GARCH Model: Estimation and Density Forecasting for Financial Returns," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 395-424.

   Cited by:

   1. Vidal-Llana, Xenxo & Guillén, Montserrat, 2022. "Cross-sectional quantile regression for estimating conditional VaR of returns during periods of high volatility," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).

Articles

1. Yuzhi Cai & Thanaset Chevapatrakul & Danilo V. Mascia, 2021. "How is price explosivity triggered in the cryptocurrency markets?," Annals of Operations Research, Springer, vol. 307(1), pages 37-51, December.

   Cited by:

   1. Assaf, Ata & Demir, Ender & Ersan, Oguz, 2024. "Detecting and date-stamping bubbles in fan tokens," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 98-113.
   2. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
   3. Abubakr Naeem, Muhammad & Iqbal, Najaf & Lucey, Brian M. & Karim, Sitara, 2022. "Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).

2. Yuzhi Cai, 2021. "Estimating expected shortfall using a quantile function model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4332-4360, July.

   Cited by:

   1. Yan Fang & Jian Li & Yinglin Liu & Yunfan Zhao, 2023. "Semiparametric estimation of expected shortfall and its application in finance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 835-851, July.

3. Zhao, Yixiu & Upreti, Vineet & Cai, Yuzhi, 2021. "Stock returns, quantile autocorrelation, and volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 73(C).

   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.

4. Yuzhi Cai & Julian Stander, 2020. "The Threshold GARCH Model: Estimation and Density Forecasting for Financial Returns," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 395-424.
   • Yuzhi Cai & Julian Stander, 2018. "The threshold GARCH model: estimation and density forecasting for financial returns," Working Papers 2018-23, Swansea University, School of Management.
   See citations under working paper version above.
5. Yuzhi Cai, 2016. "A General Quantile Function Model for Economic and Financial Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1173-1193, August.

   Cited by:

   1. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
   2. Yuzhi Cai & Guodong Li, 2018. "A novel approach to modelling the distribution of financial returns," Working Papers 2018-22, Swansea University, School of Management.

6. Lupo, P.J. & Danysh, H.E. & Symanski, E. & Langlois, P.H. & Cai, Y. & Swartz, M.D., 2015. "Neighborhood-based socioeconomic position and risk of oral clefts among offspring," American Journal of Public Health, American Public Health Association, vol. 105(12), pages 2518-2525.

   Cited by:

   1. Chunfeng Yun & Zhenjie Wang & Ping He & Chao Guo & Gong Chen & Xiaoying Zheng, 2016. "Prevalence and Parental Risk Factors for Speech Disability Associated with Cleft Palate in Chinese Children—A National Survey," IJERPH, MDPI, vol. 13(11), pages 1-8, November.
   2. Séverine Deguen & Wahida Kihal & Maxime Jeanjean & Cindy Padilla & Denis Zmirou-Navier, 2016. "Neighborhood Deprivation and Risk of Congenital Heart Defects, Neural Tube Defects and Orofacial Clefts: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-25, October.

7. Owen R Bidder & Hamish A Campbell & Agustina Gómez-Laich & Patricia Urgé & James Walker & Yuzhi Cai & Lianli Gao & Flavio Quintana & Rory P Wilson, 2014. "Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-7, February.

   Cited by:

   1. Maitreyi Sur & Tony Suffredini & Stephen M Wessells & Peter H Bloom & Michael Lanzone & Sheldon Blackshire & Srisarguru Sridhar & Todd Katzner, 2017. "Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-19, April.
   2. Jeantet, Lorène & Vigon, Vincent & Geiger, Sébastien & Chevallier, Damien, 2021. "Fully Convolutional Neural Network: A solution to infer animal behaviours from multi-sensor data," Ecological Modelling, Elsevier, vol. 450(C).

8. Yuzhi Cai & Gabriel Montes‐Rojas & Jose Olmo, 2013. "Quantile Double AR Time Series Models for Financial Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 551-560, September.

   Cited by:

   1. Zhu Huafeng & Zhang Xingfa & Liang Xin & Li Yuan, 2018. "Moving Average Model with an Alternative GARCH-Type Error," Journal of Systems Science and Information, De Gruyter, vol. 6(2), pages 165-177, April.
   2. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
   3. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
   4. Zhu, Huafeng & Zhang, Xingfa & Liang, Xin & Li, Yuan, 2017. "On a vector double autoregressive model," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 86-95.

9. Yuzhi Cai & Julian Stander & Neville Davies, 2012. "A new Bayesian approach to quantile autoregressive time series model estimation and forecasting," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(4), pages 684-698, July.

   Cited by:

   1. Yuzhi Cai & Julian Stander, 2018. "The threshold GARCH model: estimation and density forecasting for financial returns," Working Papers 2018-23, Swansea University, School of Management.
      • Yuzhi Cai & Julian Stander, 2020. "The Threshold GARCH Model: Estimation and Density Forecasting for Financial Returns," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 395-424.
   2. Griffin, Jim E. & Mitrodima, Gelly, 2020. "A Bayesian quantile time series model for asset returns," LSE Research Online Documents on Economics 105610, London School of Economics and Political Science, LSE Library.
   3. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2015. "Forecasting ICT development through quantile confidence intervals," Journal of Business Research, Elsevier, vol. 68(11), pages 2295-2298.
   4. Guodong Li & Yang Li & Chih-Ling Tsai, 2015. "Quantile Correlations and Quantile Autoregressive Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 246-261, March.
   5. Gideon Boako & Maurice Omane-Adjepong & Joseph Magnus Frimpong, 2016. "Stock Returns and Exchange Rate Nexus in Ghana: A Bayesian Quantile Regression Approach," South African Journal of Economics, Economic Society of South Africa, vol. 84(1), pages 149-179, March.
   6. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2014. "A new quantile regression forecasting model," Journal of Business Research, Elsevier, vol. 67(5), pages 779-784.

10. Yuzhi Cai, 2011. "Multi‐variate time‐series simulation," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(5), pages 566-579, September.

    Cited by:

    1. Yanira Guanche García & Maha Shadaydeh & Miguel Mahecha & Joachim Denzler, 2019. "Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(3), pages 849-867, September.
    2. M. Azimmohseni & A. R. Soltani & M. Khalafi, 2015. "Simulation of Real Discrete Time Gaussian Multivariate Stationary Processes with Given Spectral Densities," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 783-796, November.
    3. Mitra Ghanbarzadeh & Mina Aminghafari, 2016. "A Wavelet Characterization of Continuous-Time Periodically Correlated Processes with Application to Simulation," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 741-762, November.

11. Thompson, Paul & Cai, Yuzhi & Moyeed, Rana & Reeve, Dominic & Stander, Julian, 2010. "Bayesian nonparametric quantile regression using splines," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1138-1150, April.

    Cited by:

    1. Yuzhi Cai, 2016. "A Comparative Study Of Monotone Quantile Regression Methods For Financial Returns," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-16, May.
    2. Rodrigues, T. & Dortet-Bernadet, J.-L. & Fan, Y., 2019. "Simultaneous fitting of Bayesian penalised quantile splines," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 93-109.
    3. Bhattacharya, Indrabati & Ghosal, Subhashis, 2021. "Bayesian multivariate quantile regression using Dependent Dirichlet Process prior," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    4. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
    5. Philip Kostov & Sophia Davidova & Alistair Bailey, 2018. "Effect of family labour on output of farms in selected EU Member States: a non-parametric quantile regression approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(3), pages 367-395.
       • Kostov, Philip & Davidova, Sophia & Bailey, Alastair, 2016. "Effect of family labour on output of farms in selected EU Member States: A non-parametric quantile regression approach," 90th Annual Conference, April 4-6, 2016, Warwick University, Coventry, UK 236358, Agricultural Economics Society.
    6. Yun Yang & Surya T. Tokdar, 2017. "Joint Estimation of Quantile Planes Over Arbitrary Predictor Spaces," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1107-1120, July.
    7. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.
    8. Salaheddine El Adlouni, 2018. "Quantile regression C-vine copula model for spatial extremes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(1), pages 299-317, October.
    9. Yunwen Yang & Huixia Judy Wang & Xuming He, 2016. "Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood," International Statistical Review, International Statistical Institute, vol. 84(3), pages 327-344, December.

12. Yuzhi Cai, 2010. "Forecasting for quantile self-exciting threshold autoregressive time series models," Biometrika, Biometrika Trust, vol. 97(1), pages 199-208.

    Cited by:

    1. Yuzhi Cai & Julian Stander, 2018. "The threshold GARCH model: estimation and density forecasting for financial returns," Working Papers 2018-23, Swansea University, School of Management.
       • Yuzhi Cai & Julian Stander, 2020. "The Threshold GARCH Model: Estimation and Density Forecasting for Financial Returns," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 395-424.
    2. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    3. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    4. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
    5. Cathy Chen & Richard Gerlach, 2013. "Semi-parametric quantile estimation for double threshold autoregressive models with heteroskedasticity," Computational Statistics, Springer, vol. 28(3), pages 1103-1131, June.
    6. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
       • Liu, Xiaochun, 2013. "Markov-Switching Quantile Autoregression," MPRA Paper 55800, University Library of Munich, Germany.
    7. Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.
    8. Yuzhi Cai & Guodong Li, 2018. "A novel approach to modelling the distribution of financial returns," Working Papers 2018-22, Swansea University, School of Management.

13. Yuzhi Cai & Julian Stander, 2008. "Quantile self‐exciting threshold autoregressive time series models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(1), pages 186-202, January.

    Cited by:

    1. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
       • Michael Pfarrhofer, 2021. "Modeling tail risks of inflation using unobserved component quantile regressions," Papers 2103.03632, arXiv.org, revised Oct 2021.
    2. Yuzhi Cai, 2016. "A Comparative Study Of Monotone Quantile Regression Methods For Financial Returns," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-16, May.
    3. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    4. Yuzhi Cai & Thanaset Chevapatrakul & Danilo V. Mascia, 2021. "How is price explosivity triggered in the cryptocurrency markets?," Annals of Operations Research, Springer, vol. 307(1), pages 37-51, December.
    5. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
    6. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
       • Liu, Xiaochun, 2013. "Markov-Switching Quantile Autoregression," MPRA Paper 55800, University Library of Munich, Germany.
    7. Jean-Paul Chavas & Salvatore Falco, 2017. "Resilience, Weather and Dynamic Adjustments in Agroecosystems: The Case of Wheat Yield in England," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(2), pages 297-320, June.
    8. Yuzhi Cai & Guodong Li, 2018. "A novel approach to modelling the distribution of financial returns," Working Papers 2018-22, Swansea University, School of Management.

14. Cai, Yuzhi, 2007. "A quantile approach to US GNP," Economic Modelling, Elsevier, vol. 24(6), pages 969-979, November.

    Cited by:

    1. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    2. Zhu, Hui-Ming & Li, ZhaoLai & You, WanHai & Zeng, Zhaofa, 2015. "Revisiting the asymmetric dynamic dependence of stock returns: Evidence from a quantile autoregression model," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 142-153.

15. Yuzhi Cai, 2005. "A forecasting procedure for nonlinear autoregressive time series models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(5), pages 335-351.

    Cited by:

    1. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.

16. Yuzhi Cai & Neville Davies, 2003. "A simple diagnostic method of outlier detection for stationary Gaussian time series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(2), pages 205-223.

    Cited by:

    1. Palma, Wilfredo & Bondon, Pascal & Tapia, José, 2008. "Assessing influence in Gaussian long-memory models," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4487-4501, May.

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-ECM: Econometrics (2) 2018-03-12 2018-03-12
2. NEP-ETS: Econometric Time Series (2) 2018-03-12 2018-03-12
3. NEP-FOR: Forecasting (2) 2018-03-12 2018-03-12

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