IDEAS home Printed from https://ideas.repec.org/f/pya488.html
   My authors  Follow this author

Qiao Yang

Personal Details

First Name:Qiao
Middle Name:
Last Name:Yang
Suffix:
RePEc Short-ID:pya488
[This author has chosen not to make the email address public]
Terminal Degree: (from RePEc Genealogy)

Affiliation

School of Entrepreneurship and Management
ShanghaiTech University

Shanghai, China
http://sem.shanghaitech.edu.cn/
RePEc:edi:sestucn (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Maheu, John M & Yang, Qiao, 2015. "An Infinite Hidden Markov Model for Short-term Interest Rates," MPRA Paper 62408, University Library of Munich, Germany.

Articles

  1. Maheu, John M. & Yang, Qiao, 2016. "An infinite hidden Markov model for short-term interest rates," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 202-220.

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. Maheu, John M & Yang, Qiao, 2015. "An Infinite Hidden Markov Model for Short-term Interest Rates," MPRA Paper 62408, University Library of Munich, Germany.

    Cited by:

    1. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    4. Yang, Qiao, 2019. "Stock returns and real growth: A Bayesian nonparametric approach," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 53-69.
    5. Christian M. Dahl & Emma M. Iglesias, 2010. "Asymptotic normality of the QMLE in the level-effect ARCH model," CREATES Research Papers 2010-48, Department of Economics and Business Economics, Aarhus University.
    6. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    7. Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan, 2022. "Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models," International Journal of Forecasting, Elsevier, vol. 38(1), pages 51-73.
    8. Jia Liu & John M. Maheu, 2018. "Improving Markov switching models using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 297-318, April.
    9. Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.
    10. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    11. Anandadeep Mandal & Sunil S. Poshakwale & Gabriel J. Power, 2021. "Do investors gain from forecasting the asymmetric return co‐movements of financial and real assets?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3246-3268, July.
    12. Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.

Articles

  1. Maheu, John M. & Yang, Qiao, 2016. "An infinite hidden Markov model for short-term interest rates," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 202-220.
    See citations under working paper version above.Sorry, no citations of articles recorded.

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.
  1. NEP-FOR: Forecasting (2) 2015-02-28 2015-03-05. Author is listed
  2. NEP-ECM: Econometrics (1) 2015-03-05. Author is listed
  3. NEP-MFD: Microfinance (1) 2015-03-05. Author is listed
  4. NEP-ORE: Operations Research (1) 2015-03-05. Author is listed

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Qiao Yang should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.