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Beum Jo Park

Personal Details

First Name:Beum Jo
Middle Name:
Last Name:Park
Suffix:
RePEc Short-ID:ppa753

Affiliation

Department of Economics
College of Business and Economics
Dankook University

Yongin, South Korea
http://hompy.dankook.ac.kr/dkecono/

:


RePEc:edi:dedankr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Park, Beum-Jo & Kim, Myung-Joong, 2017. "A Dynamic Measure of Intentional Herd Behavior in Financial Markets," MPRA Paper 82025, University Library of Munich, Germany.

Articles

  1. Beum-Jo Park, 2016. "Investors' Herd Behavior and its Relation with Volatility in the Korean Stock Market (in Korean)," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 22(3), pages 70-93, September.
  2. Olivier Damette & Beum-Jo Park, 2015. "Tobin Tax and Volatility: A Threshold Quantile Autoregressive Regression Framework," Review of International Economics, Wiley Blackwell, vol. 23(5), pages 996-1022, November.
  3. Beum-Jo Park, 2015. "Risk Preferences in Decision Making and Cognitive Ability: An Experimental Analysis (in Korean)," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 21(2), pages 63-89, June.
  4. Park, Beum-Jo, 2014. "Time-varying, heterogeneous risk aversion and dynamics of asset prices among boundedly rational agents," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 150-159.
  5. Beum-Jo Park, 2014. "The Short-Term Risk Premium Puzzle: Revisited by Dynamic Herd Behavior (in Korean)," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 20(2), pages 1-26, June.
  6. Beum-Jo Park, 2013. "Volatility Regimes and the Relationship between Volatility, Trading Volume, and Spreads in the FX market (in Korean)," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 19(2), pages 1-23, June.
  7. Beum-Jo Park, 2012. "Dynamics of Asset Prices Based on Time-varying Risk Aversion and Adaptive Beliefs System (in Korean)," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 18(3), pages 157-187, September.
  8. Park, Beum-Jo, 2011. "Asymmetric herding as a source of asymmetric return volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2657-2665, October.
  9. Beum-Jo Park, 2011. "Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-58, September.
  10. Beum-Jo Park, 2011. "The extension of a continuous beliefs system and analyzing herd behavior in stock markets (in Korean)," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 17(2), pages 27-55, June.
  11. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.
  12. Beum-Jo Park, 2009. "Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 93-104.
  13. Beum-Jo Park, 2008. "A Study on the Relationship between Volatility and Trading Volumes Using a Surprising-Information-Stochastic-Volatility(SISV) Model (in Korean)," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 14(4), pages 47-85, December.
  14. Beum-Jo Park, 2007. "Trading Volume, Volatility, And Garch Effects In The South Korean Won/Us Dollar Exchange Market: Evidence From Conditional Quantile Estimation," The Japanese Economic Review, Japanese Economic Association, vol. 58(3), pages 382-399.
  15. Beum-Jo Park, 2007. "The Impact of Surprise Information on the Relation between Volatility and Trading Volume in Exchange Rate Markets (in Korean)," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 13(1), pages 56-87, March.
  16. Beum-Jo Park, 2002. "Asymmetric Volatility of Exchange Rate Returns Under The EMS: Some Evidence From Quantile Regression Approach for Tgarch Models," International Economic Journal, Taylor & Francis Journals, vol. 16(1), pages 105-125.
  17. Park, Beum-Jo, 2002. "An Outlier Robust GARCH Model and Forecasting Volatility of Exchange Rate Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(5), pages 381-393, August.
  18. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.

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

    Sorry, no citations of working papers recorded.

Articles

  1. Park, Beum-Jo, 2014. "Time-varying, heterogeneous risk aversion and dynamics of asset prices among boundedly rational agents," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 150-159.

    Cited by:

    1. David Feldman & Xin Xu, 2018. "Equilibrium-based volatility models of the market portfolio rate of return (peacock tails or stotting gazelles)," Annals of Operations Research, Springer, vol. 262(2), pages 493-518, March.
    2. Park, Beum-Jo & Kim, Myung-Joong, 2017. "A Dynamic Measure of Intentional Herd Behavior in Financial Markets," MPRA Paper 82025, University Library of Munich, Germany.
    3. Coqueret, Guillaume, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 180-201.
    4. He, Xue-Zhong & Li, Kai, 2015. "Profitability of time series momentum," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 140-157.
    5. Michele Berardi, 2016. "Endogenous time-varying risk aversion and asset returns," Journal of Evolutionary Economics, Springer, vol. 26(3), pages 581-601, July.
    6. Olivier Damette & Beum-Jo Park, 2015. "Tobin Tax and Volatility: A Threshold Quantile Autoregressive Regression Framework," Review of International Economics, Wiley Blackwell, vol. 23(5), pages 996-1022, November.

  2. Park, Beum-Jo, 2011. "Asymmetric herding as a source of asymmetric return volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2657-2665, October.

    Cited by:

    1. Akdoğu, Evrim & MacKay, Peter, 2012. "Product markets and corporate investment: Theory and evidence," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 439-453.
    2. T. T. Chen & B. Zheng & Y. Li & X. F. Jiang, 2017. "New approaches in agent-based modeling of complex financial systems," Papers 1703.06840, arXiv.org.
    3. Park, Beum-Jo & Kim, Myung-Joong, 2017. "A Dynamic Measure of Intentional Herd Behavior in Financial Markets," MPRA Paper 82025, University Library of Munich, Germany.
    4. Alvarez-Ramirez, J. & Alvarez, J. & Rodríguez, E., 2015. "Asymmetric long-term autocorrelations in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 330-341.
    5. Park, Beum-Jo, 2014. "Time-varying, heterogeneous risk aversion and dynamics of asset prices among boundedly rational agents," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 150-159.
    6. Olivier Damette & Stéphane Goutte, 2014. "Tobin tax and trading volume tightening: a reassessment," Working Papers halshs-00926805, HAL.
    7. Wu, Chih-Chiang & Wu, Chang-Che, 2017. "The asymmetry in carry trade and the U.S. dollar," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 304-313.
    8. Olivier Damette & Beum-Jo Park, 2015. "Tobin Tax and Volatility: A Threshold Quantile Autoregressive Regression Framework," Review of International Economics, Wiley Blackwell, vol. 23(5), pages 996-1022, November.
    9. Toshio Utsunomiya, 2013. "A new approach to the effect of intervention frequency on the foreign exchange market: evidence from Japan," Applied Economics, Taylor & Francis Journals, vol. 45(26), pages 3742-3759, September.
    10. Beum-Jo Park, 2011. "Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-58, September.
    11. Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Time-varying mixture GARCH models and asymmetric volatility," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 602-623.
    12. Cordis, Adriana S. & Kirby, Chris, 2014. "Discrete stochastic autoregressive volatility," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 160-178.
    13. Jun-jie Chen & Bo Zheng & Lei Tan, 2014. "Agent-based model with asymmetric trading and herding for complex financial systems," Papers 1407.5258, arXiv.org.
    14. Zhang, Bing & Zhou, Yun, 2015. "Asymmetries in stock marketsAuthor-Name: Wang, Peijie," European Journal of Operational Research, Elsevier, vol. 241(3), pages 749-762.
    15. Miikka Kaurijoki & Jussi Nikkinen & Janne Äijö, 2015. "Return‐Implied Volatility Dynamics of High and Low Yielding Currencies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(11), pages 1026-1041, November.

  3. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.

    Cited by:

    1. Shen, Dehua & Li, Xiao & Zhang, Wei, 2018. "Baidu news information flow and return volatility: Evidence for the Sequential Information Arrival Hypothesis," Economic Modelling, Elsevier, vol. 69(C), pages 127-133.
    2. Yang, Ann Shawing, 2016. "Calendar trading of Taiwan stock market: A study of holidays on trading detachment and interruptions," Emerging Markets Review, Elsevier, vol. 28(C), pages 140-154.
    3. Shi, Yanlin & Ho, Kin-Yip & Liu, Wai-Man, 2016. "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 291-312.
    4. Slim, Skander & Dahmene, Meriam, 2016. "Asymmetric information, volatility components and the volume–volatility relationship for the CAC40 stocks," Global Finance Journal, Elsevier, vol. 29(C), pages 70-84.
    5. Eduardo Rossi & Paolo Santucci de Magistris, 2009. "Long Memory and Tail dependence in Trading Volume and Volatility," CREATES Research Papers 2009-30, Department of Economics and Business Economics, Aarhus University.
    6. Olivier Damette & Stéphane Goutte, 2014. "Tobin tax and trading volume tightening: a reassessment," Working Papers halshs-00926805, HAL.
    7. Olivier Damette & Beum-Jo Park, 2015. "Tobin Tax and Volatility: A Threshold Quantile Autoregressive Regression Framework," Review of International Economics, Wiley Blackwell, vol. 23(5), pages 996-1022, November.
    8. Park, Beum-Jo, 2011. "Asymmetric herding as a source of asymmetric return volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2657-2665, October.
    9. Beum-Jo Park, 2011. "Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-58, September.
    10. Todorova, Neda & Souček, Michael, 2014. "The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range," Economic Modelling, Elsevier, vol. 36(C), pages 332-340.

  4. Beum-Jo Park, 2009. "Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 93-104.

    Cited by:

    1. Pierre Chausse & Dinghai Xu, 2012. "GMM Estimation of a Stochastic Volatility Model with Realized Volatility: A Monte Carlo Study," Working Papers 1203, University of Waterloo, Department of Economics, revised May 2012.
    2. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.

  5. Beum-Jo Park, 2007. "Trading Volume, Volatility, And Garch Effects In The South Korean Won/Us Dollar Exchange Market: Evidence From Conditional Quantile Estimation," The Japanese Economic Review, Japanese Economic Association, vol. 58(3), pages 382-399.

    Cited by:

    1. Park, Beum-Jo, 2014. "Time-varying, heterogeneous risk aversion and dynamics of asset prices among boundedly rational agents," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 150-159.
    2. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.
    3. Park, Beum-Jo, 2011. "Asymmetric herding as a source of asymmetric return volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2657-2665, October.
    4. Beum-Jo Park, 2011. "Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-58, September.

  6. Beum-Jo Park, 2002. "Asymmetric Volatility of Exchange Rate Returns Under The EMS: Some Evidence From Quantile Regression Approach for Tgarch Models," International Economic Journal, Taylor & Francis Journals, vol. 16(1), pages 105-125.

    Cited by:

    1. Beum-Jo Park, 2007. "Trading Volume, Volatility, And Garch Effects In The South Korean Won/Us Dollar Exchange Market: Evidence From Conditional Quantile Estimation," The Japanese Economic Review, Japanese Economic Association, vol. 58(3), pages 382-399.

  7. Park, Beum-Jo, 2002. "An Outlier Robust GARCH Model and Forecasting Volatility of Exchange Rate Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(5), pages 381-393, August.

    Cited by:

    1. Bali, Rakesh & Guirguis, Hany, 2007. "Extreme observations and non-normality in ARCH and GARCH," International Review of Economics & Finance, Elsevier, vol. 16(3), pages 332-346.
    2. F. Javier Trivez & Beatriz Catalan, 2009. "Detecting level shifts in ARMA-GARCH (1,1) Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 679-697.
    3. Angela He & Alan Wan, 2009. "Predicting daily highs and lows of exchange rates: a cointegration analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1191-1204.
    4. Carnero, M. Angeles & Peña, Daniel & Ruiz, Esther, 2012. "Estimating GARCH volatility in the presence of outliers," Economics Letters, Elsevier, vol. 114(1), pages 86-90.
    5. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    6. Wan-Hsiu Cheng, 2008. "Overestimation in the Traditional GARCH Model During Jump Periods," Economics Bulletin, AccessEcon, vol. 3(68), pages 1-20.
    7. Beum-Jo Park, 2009. "Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 93-104.
    8. Ewa Ratuszny, 2013. "Robust Estimation in VaR Modelling - Univariate Approaches using Bounded Innovation Propagation and Regression Quantiles Methodology," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 5(1), pages 35-63, March.
    9. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
    10. Mathieu Gatumel & Dominique Guegan, 2008. "Dynamic Analysis of the Insurance Linked Securities Index," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00320378, HAL.
    11. Amélie Charles, 2008. "Forecasting volatility with outliers in GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 551-565.
    12. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.
    13. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    14. Beum-Jo Park, 2007. "Trading Volume, Volatility, And Garch Effects In The South Korean Won/Us Dollar Exchange Market: Evidence From Conditional Quantile Estimation," The Japanese Economic Review, Japanese Economic Association, vol. 58(3), pages 382-399.
    15. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
    16. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. L. Grossi & G. Morelli, 2006. "Robust volatility forecasts and model selection in financial time series," Economics Department Working Papers 2006-SE02, Department of Economics, Parma University (Italy).
    18. Beatriz Catalan & F. Javier Trivez, 2007. "Forecasting volatility in GARCH models with additive outliers," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 591-596.
    19. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2008. "Estimating and Forecasting GARCH Volatility in the Presence of Outiers," Working Papers. Serie AD 2008-13, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    20. Min-Hsien Chiang & Ray Yeutien Chou & Li-Min Wang, 2016. "Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 126-144, February.

  8. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.

    Cited by:

    1. Takuma Yoshida, 2016. "Asymptotics and smoothing parameter selection for penalized spline regression with various loss functions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 278-303, November.
    2. Eric Bouye & Mark Salmon, 2009. "Dynamic copula quantile regressions and tail area dynamic dependence in Forex markets," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 721-750.
    3. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    4. Lingjie Ma & Roger Koenker, 2004. "Quantile regression methods for recursive structural equation models," CeMMAP working papers CWP01/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
    7. Crambes, Christophe & Gannoun, Ali & Henchiri, Yousri, 2013. "Support vector machine quantile regression approach for functional data: Simulation and application studies," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 50-68.
    8. Daniel Hlubinka & Miroslav Šiman, 2015. "On generalized elliptical quantiles in the nonlinear quantile regression setup," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 249-264, June.
    9. Nicholas C.S. Sim, 2009. "Modeling Quantile Dependence: A New Look at the Money-Output Relationship," School of Economics Working Papers 2009-34, University of Adelaide, School of Economics.
    10. Jiang, Xuejun & Li, Jingzhi & Xia, Tian & Yan, Wanfeng, 2016. "Robust and efficient estimation with weighted composite quantile regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 413-423.
    11. Härdle, Wolfgang K. & Song, Song, 2010. "Confidence Bands In Quantile Regression," Econometric Theory, Cambridge University Press, vol. 26(04), pages 1180-1200, August.
    12. Machado, José A.F. & Santos Silva, J.M.C. & Wei, Kehai, 2016. "Quantiles, corners, and the extensive margin of trade," European Economic Review, Elsevier, vol. 89(C), pages 73-84.
    13. Yijian Huang & Limin Peng, 2009. "Accelerated Recurrence Time Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 636-648.
    14. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
    15. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    16. Escanciano, Juan Carlos & Velasco, Carlos, 2010. "Specification tests of parametric dynamic conditional quantiles," Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
    17. Chen, Shu-Ling, 2011. "Modeling Temperature Dynamics for Aquaculture Index Insurance In Taiwan: A Nonlinear Quantile Approach," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 104229, Agricultural and Applied Economics Association.
    18. Avdulaj Krenar & Barunik Jozef, 2017. "A semiparametric nonlinear quantile regression model for financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 81-97, February.
    19. El Ghouch, Anouar & Genton, Marc G., 2009. "Local Polynomial Quantile Regression With Parametric Features," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1416-1429.
    20. Yanlin Tang & Huixia Wang & Xuming He & Zhongyi Zhu, 2012. "An informative subset-based estimator for censored quantile regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 635-655, December.
    21. Bernd Fitzenberger & Ralf A. Wilke & Xuan Zhang, 2010. "Implementing Box-Cox Quantile Regression," Econometric Reviews, Taylor & Francis Journals, vol. 29(2), pages 158-181, April.
    22. Hideo Kozumi & Genya Kobayashi, 2009. "Gibbs Sampling Methods for Bayesian Quantile Regression," Discussion Papers 2009-02, Kobe University, Graduate School of Business Administration.
    23. Baur, Dirk & Schulze, Niels, 2005. "Coexceedances in financial markets--a quantile regression analysis of contagion," Emerging Markets Review, Elsevier, vol. 6(1), pages 21-43, April.
    24. Songfeng Zheng, 2014. "A generalized Newton algorithm for quantile regression models," Computational Statistics, Springer, vol. 29(6), pages 1403-1426, December.
    25. Moshe Buchinsky & Jinyong Hahn, "undated". "Quantile Regression Model with Unknown Censoring," Working Papers _004, University of California at Berkeley, Econometrics Laboratory Software Archive.
    26. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
    27. Habshah Midi, 1999. "Preliminary estimators for robust non-linear regression estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(5), pages 591-600.
    28. M. -Y. Chen & F. -L. Lin & C. -K. Chang, 2009. "Relations between health care expenditure and income: an application of local quantile regressions," Applied Economics Letters, Taylor & Francis Journals, vol. 16(2), pages 177-181.
    29. Wang, Huixia Judy & Wang, Lan, 2009. "Locally Weighted Censored Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1117-1128.
    30. Lingjie Ma & Larry Pohlman, 2008. "Return forecasts and optimal portfolio construction: a quantile regression approach," The European Journal of Finance, Taylor & Francis Journals, vol. 14(5), pages 409-425.
    31. Simila, Timo, 2006. "Self-organizing map visualizing conditional quantile functions with multidimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 2097-2110, April.
    32. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, Elsevier.
    33. Beum-Jo Park, 2002. "Asymmetric Volatility of Exchange Rate Returns Under The EMS: Some Evidence From Quantile Regression Approach for Tgarch Models," International Economic Journal, Taylor & Francis Journals, vol. 16(1), pages 105-125.
    34. Kanamori, Takafumi & Takeuchi, Ichiro, 2006. "Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3605-3618, August.
    35. Zhou, Xiuqing & Wang, Jinde, 2005. "A genetic method of LAD estimation for models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 451-466, March.
    36. Wilke, Ralf A. & Fitzenberger, Bernd & Zhang, Xuan, 2004. "A Note on Implementing Box-Cox Quantile Regression," ZEW Discussion Papers 04-61, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    37. Díaz Iván & Rosenblum Michael, 2015. "Targeted Maximum Likelihood Estimation using Exponential Families," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 233-251, November.
    38. Bilias, Yannis & Chen, Songnian & Ying, Zhiliang, 2000. "Simple resampling methods for censored regression quantiles," Journal of Econometrics, Elsevier, vol. 99(2), pages 373-386, December.
    39. Chuan Goh, 2009. "Nonstandard Estimation of Inverse Conditional Density-Weighted Expectations," Working Papers tecipa-374, University of Toronto, Department of Economics.
    40. Thanasis Stengos & Dianqin Wang, 2007. "An algorithm for censored quantile regressions," Economics Bulletin, AccessEcon, vol. 3(1), pages 1-9.
    41. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    42. Hochgürtel, S., 1997. "Precautionary Motives and Portfolio Decisions," Discussion Paper 1997-55, Tilburg University, Center for Economic Research.
    43. He X. & Zhu L-X., 2003. "A Lack-of-Fit Test for Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1013-1022, January.
    44. Daniel Mariño-Ustacara & Luis Fernando Melo-Velandia, 2016. "Relación entre los valores en riesgo de los principales mercados financieros colombianos: un enfoque a través de modelos multivariados de regresión cuantílica," Borradores de Economia 975, Banco de la Republica de Colombia.
    45. Mukherjee, Kanchan, 2000. "Linearization Of Randomly Weighted Empiricals Under Long Range Dependence With Applications To Nonlinear Regression Quantiles," Econometric Theory, Cambridge University Press, vol. 16(03), pages 301-323, June.
    46. Rima Rajab & Milan Dražić & Nenad Mladenović & Pavle Mladenović & Keming Yu, 2015. "Fitting censored quantile regression by variable neighborhood search," Journal of Global Optimization, Springer, vol. 63(3), pages 481-500, November.
    47. Dong Jin Lee, 2009. "Testing Parameter Stability in Quantile Models: An Application to the U.S. Inflation Process," Working papers 2009-26, University of Connecticut, Department of Economics.
    48. Oberhofer, Walter & Haupt, Harry, 2003. "Nonlinear quantile regression under dependence and heterogeneity," University of Regensburg Working Papers in Business, Economics and Management Information Systems 388, University of Regensburg, Department of Economics.
    49. Thomas Q. Pedersen, 2015. "Predictable Return Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 114-132, March.
    50. Daniel Mariño Ustacara & Luis Fernando Melo Velandia, 2016. "Regresión Cuantílica Dinámica para la Medición del Valor en Riesgo: una Aplicación a Datos Colombianos," Borradores de Economia 939, Banco de la Republica de Colombia.
    51. Oberhofer, Walter & Haupt, Harry, 2005. "Consistency of nonlinear regression quantiles under Type I censoring weak dependence and general covariate design," University of Regensburg Working Papers in Business, Economics and Management Information Systems 406, University of Regensburg, Department of Economics.
    52. Heinze, Anja, 2010. "Beyond the mean gender wage gap: Decomposition of differences in wage distributions using quantile regression," ZEW Discussion Papers 10-043, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    53. Park, Jinho & Kim, Jeankyung, 2011. "Quantile regression with an epsilon-insensitive loss in a reproducing kernel Hilbert space," Statistics & Probability Letters, Elsevier, vol. 81(1), pages 62-70, January.

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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) 2017-11-05
  2. NEP-MST: Market Microstructure (1) 2017-11-05
  3. NEP-RMG: Risk Management (1) 2017-11-05

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