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Wei-Choun Yu

Personal Details

First Name:Wei-Choun
Middle Name:
Last Name:Yu
Suffix:
RePEc Short-ID:pyu64
110 Westwood Plaza, Suite C-506, Los Angeles, CA 90095
310-825-7805

Affiliation

Anderson Graduate School of Management
University of California-Los Angeles (UCLA)

Los Angeles, California (United States)
http://www.anderson.ucla.edu/

:

110 Westwood Plaza, Los Angeles, CA. 90095
RePEc:edi:aguclus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Chun-Hung Chen & Wei-Choun Yu & Eric Zivot, 2009. "Predicting Stock Volatility Using After-Hours Information," Working Papers UWEC-2009-01, University of Washington, Department of Economics.
  2. Kyongwook Choi & Wei-Choun Yu & Eric Zivot, 2008. "Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility," Working Papers UWEC-2008-20-FC, University of Washington, Department of Economics.

Articles

  1. Chen, Chun-Hung & Yu, Wei-Choun & Zivot, Eric, 2012. "Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks," International Journal of Forecasting, Elsevier, vol. 28(2), pages 366-383.
  2. Tin-Chun Lin & William Wei-Choun Yu & Yi-Chi Chen, 2012. "Determinants and probability prediction of college student retention: new evidence from the Probit model," International Journal of Education Economics and Development, Inderscience Enterprises Ltd, vol. 3(3), pages 217-236.
  3. Yi-Chi Chen & Wei-Choun Yu, 2011. "Structural change in the forward discount: a Bayesian analysis of forward rate unbiasedness hypothesis," Economics Bulletin, AccessEcon, vol. 31(2), pages 1807-1826.
  4. Yu, Wei-Choun & Zivot, Eric, 2011. "Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 579-591, April.
  5. Choi, Kyongwook & Yu, Wei-Choun & Zivot, Eric, 2010. "Long memory versus structural breaks in modeling and forecasting realized volatility," Journal of International Money and Finance, Elsevier, vol. 29(5), pages 857-875, September.
  6. Gyu-Hyen Moon & Wei-Choun Yu, 2010. "Volatility Spillovers between the US and China Stock Markets: Structural Break Test with Symmetric and Asymmetric GARCH Approaches," Global Economic Review, Taylor & Francis Journals, vol. 39(2), pages 129-149.
  7. Gyu-Hyen Moon & Wei-Choun Yu & Chung-Hyo Hong, 2009. "Dynamic hedging performance with the evaluation of multivariate GARCH models: evidence from KOSTAR index futures," Applied Economics Letters, Taylor & Francis Journals, vol. 16(9), pages 913-919.
  8. Wei-Choun Yu & Donald M. Salyards, 2009. "Parsimonious modeling and forecasting of corporate yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 73-88.
  9. Wei-Choun Yu, 2009. "Markov switching and long memory: a Monte Carlo analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 16(12), pages 1205-1210.
  10. Wei-Choun Yu, 2008. "Macroeconomic and financial market volatilities: an empirical evidence of factor model," Economics Bulletin, AccessEcon, vol. 3(33), pages 1-18.
  11. Wei-Choun Yu & Donald M. Salyards, 2008. "A Securitised Market For Human Capital," Economic Affairs, Wiley Blackwell, vol. 28(3), pages 50-56, September.

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. Kyongwook Choi & Wei-Choun Yu & Eric Zivot, 2008. "Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility," Working Papers UWEC-2008-20-FC, University of Washington, Department of Economics.

    Cited by:

    1. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.
    2. Luis A. Gil-Alana & Rangan Gupta, 2013. "Persistence and Cycles in Historical Oil Prices Data," Working Papers 201375, University of Pretoria, Department of Economics.
    3. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    4. Jerry Coakley & Jian Dollery & Neil Kellard, 2011. "Long memory and structural breaks in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(11), pages 1076-1113, November.
    5. Feng, Yuanhua & Zhou, Chen, 2015. "Forecasting financial market activity using a semiparametric fractionally integrated Log-ACD," International Journal of Forecasting, Elsevier, vol. 31(2), pages 349-363.
    6. Wang, Cindy Shin-Huei & Bauwens, Luc & Hsiao, Cheng, 2013. "Forecasting a long memory process subject to structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 171-184.
    7. Bouoiyour, jamal & Selmi, Refk, 2014. "Exchange volatility and trade performance in Morocco and Tunisia: what have we learned so far?," MPRA Paper 61602, University Library of Munich, Germany.
    8. Xiao, Weilin & Zhang, Weiguo & Zhang, Xili & Chen, Xiaoyan, 2014. "The valuation of equity warrants under the fractional Vasicek process of the short-term interest rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 320-337.
    9. Arouri, Mohamed El Hédi & Lahiani, Amine & Lévy, Aldo & Nguyen, Duc Khuong, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Energy Economics, Elsevier, vol. 34(1), pages 283-293.
    10. Fakhfekh, Mohamed & Hachicha, Nejib & Jawadi, Fredj & Selmi, Nadhem & Idi Cheffou, Abdoulkarim, 2016. "Measuring volatility persistence for conventional and Islamic banks: An FI-EGARCH approach," Emerging Markets Review, Elsevier, vol. 27(C), pages 84-99.
    11. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    12. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
    13. Mohamed El Hedi Arouri & Shawkat Hammoudeh & Amine Lahiani & Duc Khuong Nguyen, 2013. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," Working Papers hal-00798033, HAL.
    14. Kellard, Neil M. & Jiang, Ying & Wohar, Mark, 2015. "Spurious long memory, uncommon breaks and the implied–realized volatility puzzle," Journal of International Money and Finance, Elsevier, vol. 56(C), pages 36-54.
    15. Al-Shboul, Mohammad & Anwar, Sajid, 2016. "Fractional integration in daily stock market indices at Jordan's Amman stock exchange," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 16-37.
    16. Omane-Adjepong, Maurice & Boako, Gideon, 2017. "Long-range dependence in returns and volatility of global gold market amid financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 188-202.
    17. Bouoiyour, Jamal & Selmi, Refk, 2014. "Exchange Uncertainty and Export Performance in Egypt: New Insights from Wavelet Decomposition and Optimal GARCH Model," MPRA Paper 59568, University Library of Munich, Germany, revised 2014.
    18. Igor LEBRUN & Ludovic DOBBELAERE, "undated". "A Macro-econometric Model for the Economy of Lesotho," EcoMod2010 259600102, EcoMod.
    19. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    20. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.
    21. Jung, R.C. & Maderitsch, R., 2014. "Structural breaks in volatility spillovers between international financial markets: Contagion or mere interdependence?," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 331-342.
    22. Sergii Pypko, 2015. "Volatility Forecast in Crises and Expansions," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 8(3), pages 1-26, August.
    23. Samet Günay & Yanlin Shi, 2016. "Long-Memory in Volatilities of CDS Spreads: Evidences from the Emerging Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 122-137, March.
    24. Ke Yang & Langnan Chen, 2014. "Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect," International Review of Finance, International Review of Finance Ltd., vol. 14(3), pages 345-392, September.
    25. WANG, Shin-Huei & BAUWENS, Luc & HSIAO, Cheng, 2012. "Forecasting long memory processes subject to structural breaks," CORE Discussion Papers 2012048, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    26. Agata Kliber, 2013. "Influence of the Greek Crisis on the Risk Perception of European Economies," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 5(2), pages 125-161, June.
    27. Kumar, Dilip, 2017. "Realized volatility transmission from crude oil to equity sectors: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 149-167.
    28. Demos, Guilherme & Da Silva, Sergio & Matsushita, Raul, 2015. "Some Statistical Properties of the Mini Flash Crashes," MPRA Paper 65473, University Library of Munich, Germany.

Articles

  1. Chen, Chun-Hung & Yu, Wei-Choun & Zivot, Eric, 2012. "Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks," International Journal of Forecasting, Elsevier, vol. 28(2), pages 366-383.

    Cited by:

    1. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2016. "Forecasting stock volatility using after-hour information: Evidence from the Australian Stock Exchange," Economic Modelling, Elsevier, vol. 52(PB), pages 592-608.
    2. Todorova, Neda & Souček, Michael, 2014. "Overnight information flow and realized volatility forecasting," Finance Research Letters, Elsevier, vol. 11(4), pages 420-428.
    3. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    4. Victor Bello Accioly & Beatriz Vaz de Melo Mendes, 2016. "Assessing the Impact of the Realized Range on the (E)GARCH Volatility: Evidence from Brazil," Brazilian Business Review, Fucape Business School, vol. 13(2), pages 1-26, March.

  2. Tin-Chun Lin & William Wei-Choun Yu & Yi-Chi Chen, 2012. "Determinants and probability prediction of college student retention: new evidence from the Probit model," International Journal of Education Economics and Development, Inderscience Enterprises Ltd, vol. 3(3), pages 217-236.

    Cited by:

    1. Lutz Hendricks & Oksana Leukhina, 2017. "Online Appendix to "How Risky is College Investment?"," Technical Appendices 15-52, Review of Economic Dynamics.
    2. Lutz Hendricks & Oksana Leukhina, 2015. "How Risky is College Investment?," CESifo Working Paper Series 5203, CESifo Group Munich.

  3. Yi-Chi Chen & Wei-Choun Yu, 2011. "Structural change in the forward discount: a Bayesian analysis of forward rate unbiasedness hypothesis," Economics Bulletin, AccessEcon, vol. 31(2), pages 1807-1826.

    Cited by:

    1. Tsung-Wu Ho & Wan-Shin Mo, 2016. "Testing the Persistence of the Forward Premium: Structural Changes or Misspecification?," Open Economies Review, Springer, vol. 27(1), pages 119-138, February.

  4. Yu, Wei-Choun & Zivot, Eric, 2011. "Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 579-591, April.

    Cited by:

    1. Rocío Elizondo, 2013. "Forecasting the Term Structure of Interest Rates in Mexico Using an Affine Model," Working Papers 2013-03, Banco de México.
    2. Dauwe, Alexander & Moura, Marcelo L., 2011. "Forecasting the term structure of the Euro Market using Principal Component Analysis," Insper Working Papers wpe_233, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    3. Karimalis, Emmanouil & Kosmidis, Ioannis & Peters, Gareth, 2017. "Multi yield curve stress-testing framework incorporating temporal and cross tenor structural dependencies," Bank of England working papers 655, Bank of England.
    4. Caldeira, João F. & Laurini, Márcio P. & Portugal, Marcelo S., 2010. "Bayesian Inference Applied to Dynamic Nelson-Siegel Model with Stochastic Volatility," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.
    5. Constantino Hevia & Martin Gonzalez‐Rozada & Martin Sola & Fabio Spagnolo, 2015. "Estimating and Forecasting the Yield Curve Using A Markov Switching Dynamic Nelson and Siegel Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(6), pages 987-1009, September.
    6. Levant, Jared & Ma, Jun, 2017. "A dynamic Nelson-Siegel yield curve model with Markov switching," Economic Modelling, Elsevier, vol. 67(C), pages 73-87.
    7. Kaya, Huseyin, 2013. "Forecasting the yield curve and the role of macroeconomic information in Turkey," Economic Modelling, Elsevier, vol. 33(C), pages 1-7.
    8. Anthony H. Tu & Cathy Yi-Hsuan Chen, 2016. "What Derives the Bond Portfolio Value-at-Risk: Information Roles of Macroeconomic and Financial Stress Factors," SFB 649 Discussion Papers SFB649DP2016-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Cem Çakmakli, 2012. "Bayesian Semiparametric Dynamic Nelson-Siegel Model," Working Paper series 59_12, Rimini Centre for Economic Analysis, revised Sep 2012.

  5. Choi, Kyongwook & Yu, Wei-Choun & Zivot, Eric, 2010. "Long memory versus structural breaks in modeling and forecasting realized volatility," Journal of International Money and Finance, Elsevier, vol. 29(5), pages 857-875, September.
    See citations under working paper version above.
  6. Gyu-Hyen Moon & Wei-Choun Yu, 2010. "Volatility Spillovers between the US and China Stock Markets: Structural Break Test with Symmetric and Asymmetric GARCH Approaches," Global Economic Review, Taylor & Francis Journals, vol. 39(2), pages 129-149.

    Cited by:

    1. Yavas, Burhan F. & Dedi, Lidija, 2016. "An investigation of return and volatility linkages among equity markets: A study of selected European and emerging countries," Research in International Business and Finance, Elsevier, vol. 37(C), pages 583-596.
    2. David E. Allen & Michael McAleer & Robert J. Powell & Abhay K. Singh, 2014. "Volatility Spillovers from Australia's Major Trading Partners across the GFC," Tinbergen Institute Discussion Papers 14-106/III, Tinbergen Institute.
    3. Long, Ling & Tsui, Albert K. & Zhang, Zhaoyong, 2014. "Conditional heteroscedasticity with leverage effect in stock returns: Evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 37(C), pages 89-102.
    4. Li, Hong, 2012. "The impact of China's stock market reforms on its international stock market linkages," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 358-368.
    5. Allen, D.E. & McAleer, M.J. & Amram, R., 2011. "Volatility Spillovers from the Chinese Stock Market to Economic Neighbours," Econometric Institute Research Papers EI 2011-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. David E. Allen & Michael McAleer & Robert J. Powell & Abhay K. Singh, 2012. "Volatility Spillovers from the US to Australia and China across the GFC," Documentos de Trabajo del ICAE 2012-30, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    7. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.
    8. M. Fatih Oztek & Nadir Ocal, 2012. "Integration of China Stock Markets with International Stock Markets: An application of Smooth Transition Conditional Correlation with Double Transition Functions," ERC Working Papers 1209, ERC - Economic Research Center, Middle East Technical University, revised Dec 2012.
    9. Hou, Yang & Li, Steven, 2016. "Information transmission between U.S. and China index futures markets: An asymmetric DCC GARCH approach," Economic Modelling, Elsevier, vol. 52(PB), pages 884-897.
    10. Li, Johnny Siu-Hang & Ng, Andrew C.Y. & Chan, Wai-Sum, 2015. "Managing financial risk in Chinese stock markets: Option pricing and modeling under a multivariate threshold autoregression," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 217-230.
    11. Mehmet Fatih Öztek & Nadir Öcal, 2016. "The effects of domestic and international news and volatility on integration of Chinese stock markets with international stock markets," Empirical Economics, Springer, vol. 50(2), pages 317-360, March.

  7. Gyu-Hyen Moon & Wei-Choun Yu & Chung-Hyo Hong, 2009. "Dynamic hedging performance with the evaluation of multivariate GARCH models: evidence from KOSTAR index futures," Applied Economics Letters, Taylor & Francis Journals, vol. 16(9), pages 913-919.

    Cited by:

    1. Onur Olgun & I. Hakan Yetkiner, 2011. "Determination of Optimal Hedging Strategy for Index Futures: Evidence from Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 68-79, November.
    2. Liu, Xiaochun & Jacobsen, Brian, 2011. "The Dynamic International Optimal Hedge Ratio," MPRA Paper 35260, University Library of Munich, Germany.
    3. Caporin, Massimiliano, 2013. "Equity and CDS sector indices: Dynamic models and risk hedging," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 261-275.

  8. Wei-Choun Yu & Donald M. Salyards, 2009. "Parsimonious modeling and forecasting of corporate yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 73-88.

    Cited by:

    1. Yifeng Yan & Ju'e Guo, 2015. "The Sovereign Yield Curve and the Macroeconomy in China," Pacific Economic Review, Wiley Blackwell, vol. 20(3), pages 415-441, August.
    2. Yu, Wei-Choun & Zivot, Eric, 2011. "Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 579-591, April.
    3. Levant, Jared & Ma, Jun, 2017. "A dynamic Nelson-Siegel yield curve model with Markov switching," Economic Modelling, Elsevier, vol. 67(C), pages 73-87.
    4. Eran Raviv, 2013. "Prediction Bias Correction for Dynamic Term Structure Models," Tinbergen Institute Discussion Papers 13-041/III, Tinbergen Institute.
    5. Anthony H. Tu & Cathy Yi-Hsuan Chen, 2016. "What Derives the Bond Portfolio Value-at-Risk: Information Roles of Macroeconomic and Financial Stress Factors," SFB 649 Discussion Papers SFB649DP2016-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Alexey Akimov & Simon Stevenson & Maxim Zagonov, 2015. "Public Real Estate and the Term Structure of Interest Rates: A Cross-Country Study," The Journal of Real Estate Finance and Economics, Springer, vol. 51(4), pages 503-540, November.

  9. Wei-Choun Yu, 2009. "Markov switching and long memory: a Monte Carlo analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 16(12), pages 1205-1210.

    Cited by:

    1. Baek, Changryong & Fortuna, Natércia & Pipiras, Vladas, 2014. "Can Markov switching model generate long memory?," Economics Letters, Elsevier, vol. 124(1), pages 117-121.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper 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-FMK: Financial Markets (1) 2009-01-24
  2. NEP-FOR: Forecasting (1) 2009-01-24
  3. NEP-MST: Market Microstructure (1) 2009-01-24
  4. NEP-RMG: Risk Management (1) 2009-01-24

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