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Chun Liu

Not to be confused with: Chun Chu Liu

Personal Details

First Name:Chun
Middle Name:
Last Name:Liu
Suffix:
RePEc Short-ID:pli412
Terminal Degree:2007 Department of Economics; University of Toronto (from RePEc Genealogy)

Affiliation

School of Economics and Management
Tsinghua University

Beijing, China
http://www.sem.tsinghua.edu.cn/
RePEc:edi:setsicn (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Zhuo Chen & Zhiguo He & Chun Liu, 2017. "The Financing of Local Government in China: Stimulus Loan Wanes and Shadow Banking Waxes," NBER Working Papers 23598, National Bureau of Economic Research, Inc.
  2. Liu, Chun, 2010. "Marginal likelihood calculation for gelfand-dey and Chib Method," MPRA Paper 34928, University Library of Munich, Germany.
  3. Chun Liu & John M Maheu, 2010. "Intraday Dynamics of Volatility and Duration: Evidence from the Chinese Stock Market," Working Papers tecipa-401, University of Toronto, Department of Economics.
  4. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
  5. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers tecipa-304, University of Toronto, Department of Economics.

Articles

  1. Liu, Chun & Maheu, John M., 2012. "Intraday dynamics of volatility and duration: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 20(3), pages 329-348.
  2. Liu, Chun & Liu, Qing, 2012. "Marginal likelihood calculation for the Gelfand–Dey and Chib methods," Economics Letters, Elsevier, vol. 115(2), pages 200-203.
  3. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
  4. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(3), pages 326-360, Summer.

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. Zhuo Chen & Zhiguo He & Chun Liu, 2017. "The Financing of Local Government in China: Stimulus Loan Wanes and Shadow Banking Waxes," NBER Working Papers 23598, National Bureau of Economic Research, Inc.

    Cited by:

    1. Markus K. Brunnermeier & Michael Sockin & Wei Xiong, 2017. "China's Gradualistic Economic Approach and Financial Markets," NBER Working Papers 23194, National Bureau of Economic Research, Inc.
    2. Allen, Franklin & Qian, Yiming & Tu, Guoqian & Yu, Frank, 2019. "Entrusted loans: A close look at China's shadow banking system," Journal of Financial Economics, Elsevier, vol. 133(1), pages 18-41.
    3. Jianchao Fan & Jing Liu & Yinggang Zhou, 2021. "Investing like conglomerates: is diversification a blessing or curse for China's local governments?," BIS Working Papers 920, Bank for International Settlements.
    4. Marlene Amstad & Zhiguo He, 2019. "Chinese Bond Market and Interbank Market," NBER Working Papers 25549, National Bureau of Economic Research, Inc.
    5. Cai, Yue, 2021. "Expansionary monetary policy and credit allocation: Evidence from China," China Economic Review, Elsevier, vol. 66(C).
    6. Chen, Zhuo & He, Zhiguo & Liu, Chun, 2020. "The financing of local government in China: Stimulus loan wanes and shadow banking waxes," Journal of Financial Economics, Elsevier, vol. 137(1), pages 42-71.
    7. Yi Huang & Jianjun Miao & Pengfei Wang, 2016. "Saving China's Stock Market," IHEID Working Papers 09-2016, Economics Section, The Graduate Institute of International Studies.
    8. Wei Xiong, 2018. "The Mandarin Model of Growth," NBER Working Papers 25296, National Bureau of Economic Research, Inc.
    9. Allen, Franklin & Qian, Jun & Qian, Meijun, 2018. "A Review of China's Institutions," CEPR Discussion Papers 13269, C.E.P.R. Discussion Papers.
    10. Guonan Ma & Jinzhao Chen, 2019. "The Role of Internally Financed Capex in Rising Chinese Corporate Debts," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 61(3), pages 413-442, September.
    11. Chunjing Wang & Jinming Qu, 2020. "Analysis of the Pro-cyclical Behavior of Credit Spread in Chinese Bond Market," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 10(4), pages 1-8.
    12. Kaiji Chen & Haoyu Gao & Patrick C. Higgins & Daniel F. Waggoner & Tao Zha, 2020. "Monetary Stimulus Amidst the Infrastructure Investment Spree: Evidence from China's Loan-Level Data," NBER Working Papers 27763, National Bureau of Economic Research, Inc.
    13. Vinh Q. T. Dang & Isaac Otchere & Erin P. K. So & Isabel K. M. Yan, 2021. "Not all shadow banking is bad! Evidence from credit intermediation of non-financial Chinese firms," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1437-1462, November.
    14. Zhang, Xiaoqian & Wang, Zhiwei, 2020. "Marketization vs. market chase: Insights from implicit government guarantees," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 435-455.
    15. Mo, Jiawei, 2018. "Land financing and economic growth: Evidence from Chinese counties," China Economic Review, Elsevier, vol. 50(C), pages 218-239.
    16. Francesco D'Acunto & Michael Weber & Jin Xie & Michael Weber, 2019. "Punish One, Teach A Hundred: The Sobering Effect of Punishment on the Unpunished," CESifo Working Paper Series 7512, CESifo.
    17. Song, Zheng (Michael) & Xiong, Wei, 2018. "Risks in China’s financial system," BOFIT Discussion Papers 1/2018, Bank of Finland, Institute for Economies in Transition.
    18. Kaiji Chen & Jue Ren & Tao Zha, 2018. "The Nexus of Monetary Policy and Shadow Banking in China," American Economic Review, American Economic Association, vol. 108(12), pages 3891-3936, December.
    19. Mengzhu Zhang & Si Qiao & Xiang Yan, 2021. "The secondary circuit of capital and the making of the suburban property boom in postcrisis Chinese cities," Environment and Planning A, , vol. 53(6), pages 1331-1355, September.
    20. Huang, Yi & Pagano, Marco & Panizza, Ugo, 2019. "Local crowding out in China," CFS Working Paper Series 632, Center for Financial Studies (CFS).
    21. Bo Li & Jacopo Ponticelli, 2020. "Going Bankrupt in China," NBER Working Papers 27501, National Bureau of Economic Research, Inc.
    22. Ouyang, Min & Zhang, Shengxing, 2020. "Corruption as Collateral," MPRA Paper 98635, University Library of Munich, Germany.
    23. Ridoy Deb Nath & Mohammad Ashraful Ferdous Chowdhury, 2021. "Shadow banking: a bibliometric and content analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
    24. Torsten Ehlers & Steven Kong & Feng Zhu, 2018. "Mapping shadow banking in China: structure and dynamics," BIS Working Papers 701, Bank for International Settlements.
    25. Kaiji Chen & Tao Zha, 2018. "Macroeconomic Effects of China's Financial Policies," FRB Atlanta Working Paper 2018-12, Federal Reserve Bank of Atlanta.
    26. Walker, Thomas & Zhang, Xueying & Zhang, Aoran & Wang, Yulin, 2021. "Fact or fiction: Implicit government guarantees in China’s corporate bond market," Journal of International Money and Finance, Elsevier, vol. 116(C).
    27. Xiang Xu & Alice Siqi Han, 2018. "Will China Collapse: A Review, Assessment And Outlook," Economics Working Papers 18104, Hoover Institution, Stanford University.
    28. Ming Lu & Huiyong Zhong, 2018. "Eurozonization of the Chinese Economy: How Do Intergovernmental Transfers Affect Local Government Debt in China?," Asian Economic Papers, MIT Press, vol. 17(1), pages 1-18, Winter/Sp.
    29. Carpenter, Jennifer N. & Lu, Fangzhou & Whitelaw, Robert F., 2021. "The real value of China’s stock market," Journal of Financial Economics, Elsevier, vol. 139(3), pages 679-696.
    30. Gao, Haoyu & Ru, Hong & Tang, Dragon Yongjun, 2021. "Subnational debt of China: The politics-finance nexus," Journal of Financial Economics, Elsevier, vol. 141(3), pages 881-895.
    31. Luo, Ronghua & Fang, Hongyan & Liu, Jinjin & Zhao, Senyang, 2019. "Maturity mismatch and incentives: Evidence from bank issued wealth management products in China," Journal of Banking & Finance, Elsevier, vol. 107(C), pages 1-1.
    32. Min Zhang & Yahong Zhang, 2020. "Monetary Stimulus Policy in China: the Bank Credit Channel," Working Papers 2001, University of Windsor, Department of Economics.
    33. Qian, Ningyu, 2018. "Anti-corruption effects on the credit risk of local financing vehicles and the pricing of Chengtou bonds: Evidence from a quasi-natural experiment in China," Finance Research Letters, Elsevier, vol. 26(C), pages 162-168.
    34. Feng, Xu & Lu, Lei & Xiao, Yajun, 2020. "Shadow banks, leverage risks, and asset prices," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).

  2. Liu, Chun, 2010. "Marginal likelihood calculation for gelfand-dey and Chib Method," MPRA Paper 34928, University Library of Munich, Germany.

    Cited by:

  3. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.

    Cited by:

    1. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    2. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    3. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    4. Yafeng Shi & Tingting Ying & Yanlong Shi & Chunrong Ai, 2020. "A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1025-1034, November.
    5. Antonello Loddo & Shawn Ni & Dongchu Sun, 2011. "Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 342-355, July.
    6. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    7. Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Aug 2021.
    8. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    9. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    10. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    11. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
    12. Tian Xie, 2012. "Least Squares Model Averaging By Prediction Criterion," Working Paper 1299, Economics Department, Queen's University.
    13. 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.
    14. Andrada-Félix, Julián & Fernández-Rodríguez, Fernando & Fuertes, Ana-Maria, 2016. "Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay?," International Journal of Forecasting, Elsevier, vol. 32(3), pages 695-715.
    15. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    16. Donghua Wang & Yang Xin & Xiaohui Chang & Xingze Su, 2021. "Realized volatility forecasting and volatility spillovers: Evidence from Chinese non‐ferrous metals futures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2713-2731, April.
    17. Ana-Maria Fuertes & Elena Kalotychou & Natasa Todorovic, 2015. "Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 251-278, August.
    18. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    19. Ji‐Eun Choi & Dong Wan Shin, 2018. "Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 691-704, September.
    20. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    21. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    22. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01442618, HAL.
    23. Wang, Chengyang & Nishiyama, Yoshihiko, 2015. "Volatility forecast of stock indices by model averaging using high-frequency data," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 324-337.
    24. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    25. Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
    26. Nima Nonejad, 2013. "A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory," CREATES Research Papers 2013-24, Department of Economics and Business Economics, Aarhus University.
    27. Chew Lian Chua & Sandy Suardi & Sarantis Tsiaplias, 2011. "Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement?," Melbourne Institute Working Paper Series wp2011n01, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    28. Yoontae Jeon & Thomas H. McCurdy, 2017. "Time-Varying Window Length for Correlation Forecasts," Econometrics, MDPI, Open Access Journal, vol. 5(4), pages 1-29, December.
    29. Elena Andreou & Constantinos Kourouyiannis & Andros Kourtellos, 2012. "Volatility Forecast Combinations using Asymmetric Loss Functions," University of Cyprus Working Papers in Economics 07-2012, University of Cyprus Department of Economics.
    30. Vasyl Golosnoy & Yarema Okhrin, 2015. "Using information quality for volatility model combinations," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1055-1073, June.
    31. 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.
    32. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 9(3), pages 1-20, September.
    33. Dimitrios Louzis & Spyros Xanthopoulos-Sisinis & Apostolos Refenes, 2011. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Post-Print hal-00709559, HAL.
    34. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).
    35. Chua, Chew Lian & Suardi, Sandy & Tsiaplias, Sarantis, 2013. "Predicting short-term interest rates using Bayesian model averaging: Evidence from weekly and high frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 442-455.
    36. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    37. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.

  4. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers tecipa-304, University of Toronto, Department of Economics.

    Cited by:

    1. Bauwens, Luc & Rombouts, Jeroen V.K., 2012. "On marginal likelihood computation in change-point models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3415-3429.
    2. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    3. Nonejad, Nima, 2014. "Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks," MPRA Paper 55664, University Library of Munich, Germany.
    4. 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.
    5. Becker, Janis & Leschinski, Christian & Sibbertsen, Philipp, 2019. "Robust Multivariate Local Whittle Estimation and Spurious Fractional Cointegration," Hannover Economic Papers (HEP) dp-660, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. Liu, Chun, 2010. "Marginal likelihood calculation for gelfand-dey and Chib Method," MPRA Paper 34928, University Library of Munich, Germany.
    7. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 25-41.
    8. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.
    9. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    10. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
    11. Julien Chevallier & Yannick Le Pen & Benoît Sévi, 2009. "Options introduction and volatility in the EU ETS," EconomiX Working Papers 2009-33, University of Paris Nanterre, EconomiX.
    12. Julien Chevallier & Benoît Sévi, 2009. "On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting," Working Papers 2009.113, Fondazione Eni Enrico Mattei.
    13. Maheu, John & Song, Yong, 2012. "A new structural break model with application to Canadian inflation forecasting," MPRA Paper 36870, University Library of Munich, Germany.
    14. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
    15. 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.
    16. Goldman Elena & Nam Jouahn & Tsurumi Hiroki & Wang Jun, 2013. "Regimes and long memory in realized volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 521-549, December.
    17. Davide De Gaetano, 2016. "Forecast Combinations For Realized Volatility In Presence Of Structural Breaks," Departmental Working Papers of Economics - University 'Roma Tre' 0208, Department of Economics - University Roma Tre.
    18. Casson, Catherine & Fry, J. M. & Casson, Mark, 2011. "Evolution or revolution? a study of price and wage volatility in England, 1200-1900," MPRA Paper 31518, University Library of Munich, Germany.
    19. Donghua Wang & Yang Xin & Xiaohui Chang & Xingze Su, 2021. "Realized volatility forecasting and volatility spillovers: Evidence from Chinese non‐ferrous metals futures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2713-2731, April.
    20. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
    21. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    22. Nima Nonejad, 2013. "Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach," CREATES Research Papers 2013-26, Department of Economics and Business Economics, Aarhus University.
    23. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
    24. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.
    25. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    26. Aalborg, Halvor Aarhus & Molnár, Peter & de Vries, Jon Erik, 2019. "What can explain the price, volatility and trading volume of Bitcoin?," Finance Research Letters, Elsevier, vol. 29(C), pages 255-265.
    27. Yudong Wang & Zhiyuan Pan & Chongfeng Wu, 2017. "Time‐Varying Parameter Realized Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 566-580, August.
    28. Song, Junmo & Baek, Changryong, 2019. "Detecting structural breaks in realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 58-75.
    29. Lin, Edward M.H. & Chen, Cathy W.S. & Gerlach, Richard, 2012. "Forecasting volatility with asymmetric smooth transition dynamic range models," International Journal of Forecasting, Elsevier, vol. 28(2), pages 384-399.
    30. Arnaud Dufays & Jeroen V. K. Rombouts, 2019. "Sparse Change-point HAR Models for Realized Variance," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 857-880, September.
    31. Nima Nonejad, 2013. "A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory," CREATES Research Papers 2013-24, Department of Economics and Business Economics, Aarhus University.
    32. Li, Wenlan & Cheng, Yuxiang & Fang, Qiang, 2020. "Forecast on silver futures linked with structural breaks and day-of-the-week effect," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    33. Yoontae Jeon & Thomas H. McCurdy, 2017. "Time-Varying Window Length for Correlation Forecasts," Econometrics, MDPI, Open Access Journal, vol. 5(4), pages 1-29, December.
    34. 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.
    35. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    36. 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.
    37. Markopoulou, Chrysi E. & Skintzi, Vasiliki D. & Refenes, Apostolos-Paul N., 2016. "Realized hedge ratio: Predictability and hedging performance," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 121-133.
    38. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
    39. AitSahlia, Farid & Yoon, Joon-Hui, 2016. "Information stages in efficient markets," Journal of Banking & Finance, Elsevier, vol. 69(C), pages 84-94.
    40. Chevallier, Julien, 2011. "Detecting instability in the volatility of carbon prices," Energy Economics, Elsevier, vol. 33(1), pages 99-110, January.
    41. Ying Chen & Bo Li, 2011. "Forecasting Yield Curves in an Adaptive Framework," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 3(4), pages 237-259, December.
    42. Nonejad, Nima, 2017. "Parameter instability, stochastic volatility and estimation based on simulated likelihood: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 61(C), pages 388-408.
    43. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
    44. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    45. Xiangjin B. Chen & Jiti Gao & Degui Li & Param Silvapulle, 2018. "Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 88-100, January.
    46. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
    47. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.

Articles

  1. Liu, Chun & Maheu, John M., 2012. "Intraday dynamics of volatility and duration: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 20(3), pages 329-348.

    Cited by:

    1. Doojin Ryu, 2013. "Spread and depth adjustment process: an analysis of high-quality microstructure data," Applied Economics Letters, Taylor & Francis Journals, vol. 20(16), pages 1506-1510, November.
    2. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    3. Chung, Kee H. & Park, Seongkyu “Gilbert” & Ryu, Doojin, 2016. "Trade duration, informed trading, and option moneyness," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 395-411.
    4. Thomas Dimpfl & Stefania Odelli, 2020. "Bitcoin Price Risk—A Durations Perspective," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(7), pages 1-18, July.
    5. Doojin Ryu, 2015. "Information content of inter-transaction time: A structural approach," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 16(4), pages 697-711, August.
    6. Karaa, Rabaa & Slim, Skander & Hmaied, Dorra Mezzez, 2018. "Trading intensity and the volume-volatility relationship on the Tunis Stock Exchange," Research in International Business and Finance, Elsevier, vol. 44(C), pages 88-99.
    7. Roman Huptas, 2016. "The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations – the Bayesian Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 1-20, March.
    8. Doojin Ryu, 2017. "Comprehensive market microstructure model: considering the inventory holding costs," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(2), pages 183-201, March.
    9. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.

  2. Liu, Chun & Liu, Qing, 2012. "Marginal likelihood calculation for the Gelfand–Dey and Chib methods," Economics Letters, Elsevier, vol. 115(2), pages 200-203.
    See citations under working paper version above.
  3. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
    See citations under working paper version above.
  4. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(3), pages 326-360, Summer.
    See citations under working paper version above.

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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 4 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-ETS: Econometric Time Series (3) 2008-01-05 2008-04-15 2010-04-17
  2. NEP-MST: Market Microstructure (3) 2008-01-05 2008-04-15 2010-04-17
  3. NEP-ECM: Econometrics (2) 2008-01-05 2008-04-15
  4. NEP-TRA: Transition Economics (2) 2010-04-17 2017-07-30
  5. NEP-BAN: Banking (1) 2017-07-30
  6. NEP-CBA: Central Banking (1) 2008-04-15
  7. NEP-CNA: China (1) 2017-07-30
  8. NEP-FMK: Financial Markets (1) 2008-01-05
  9. NEP-FOR: Forecasting (1) 2008-04-15
  10. NEP-RMG: Risk Management (1) 2008-04-15

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