Philip L.H. Yu

Personal Details

First Name:	Philip
Middle Name:	L.H.
Last Name:	Yu
Suffix:
RePEc Short-ID:	pyu43
	http://web.hku.hk/~plhyu

Affiliation

The University of Hong Kong, Department of Statistics and Actuarial Science

http://www.hku.hk/statistics
Hong Kong, China

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. Joseph K.W. Fung & Philip Yu, 2007. "Order Imbalance and the Dynamics of Index and Futures Prices," Working Papers 072007, Hong Kong Institute for Monetary Research.

   Cited by:

   1. Wang, Xiaolin & Ye, Qiang & Zhao, Feng, 2016. "Trading activity and price behavior in Chinese agricultural futures markets," Finance Research Letters, Elsevier, vol. 18(C), pages 52-59.
   2. Lam, Kin & Lean, Hooi Hooi & Wong, Wing-Keung, 2016. "Stochastic Dominance and Investors’ Behavior towards Risk: The Hong Kong Stocks and Futures Markets," MPRA Paper 74386, University Library of Munich, Germany.
   3. Wu, Lei & Zeng, Hongchao, 2019. "The impact of liquidity constraints on the cash-futures basis dynamics: Evidence from the Chinese market," Economic Modelling, Elsevier, vol. 83(C), pages 96-110.
   4. Qiang Liu & Gaoxiu Qiao, 2017. "The evolving nature of intraday price discovery in the Chinese CSI 300 index futures market," Empirical Economics, Springer, vol. 52(4), pages 1569-1585, June.
   5. Chen, Chen & Lee, Hsiu-Chuan & Liao, Tzu-Hsiang, 2016. "Risk-neutral skewness and market returns: The role of institutional investor sentiment in the futures market," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 203-225.

Articles

1. Cathy Chen & Simon Lin & Philip Yu, 2012. "Smooth Transition Quantile Capital Asset Pricing Models with Heteroscedasticity," Computational Economics, Springer;Society for Computational Economics, vol. 40(1), pages 19-48, June.

   Cited by:

   1. Cathy W. S. Chen & Mike K. P. So & Thomas C. Chiang, 2016. "Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach," The Japanese Economic Review, Springer, vol. 67(1), pages 96-124, March.
      • Cathy W.S. Chen & Mike K.P. So & Thomas C. Chiang, 2016. "Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach," The Japanese Economic Review, Japanese Economic Association, vol. 67(1), pages 96-124, March.
   2. 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.
   3. Derek Bunn, Arne Andresen, Dipeng Chen, Sjur Westgaard, 2016. "Analysis and Forecasting of Electricty Price Risks with Quantile Factor Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
   4. Marco Bottone & Mauro Bernardi & Lea Petrella, 2019. "Unified Bayesian Conditional Autoregressive Risk Measures using the Skew Exponential Power Distribution," Papers 1902.03982, arXiv.org, revised Sep 2019.
   5. Cathy W. S. Chen & Muyi Li & Nga T. H. Nguyen & Songsak Sriboonchitta, 2017. "On Asymmetric Market Model with Heteroskedasticity and Quantile Regression," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 155-174, January.

2. Lee, Paul H. & Yu, Philip L.H., 2012. "Mixtures of weighted distance-based models for ranking data with applications in political studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2486-2500.

   Cited by:

   1. Biernacki, Christophe & Jacques, Julien, 2013. "A generative model for rank data based on insertion sort algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 162-176.
   2. Irurozki, Ekhine & Calvo, Borja & Lozano, Jose A., 2016. "PerMallows: An R Package for Mallows and Generalized Mallows Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i12).
   3. Andrea Bonanomi & Marta Nai Ruscone & Silvia Angela Osmetti, 2017. "Defining subjects distance in hierarchical cluster analysis by copula approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 859-872, March.
   4. Xu, Hang & Alvo, Mayer & Yu, Philip L.H., 2018. "Angle-based models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 113-136.

3. Cheng, Xixin & Li, W.K. & Yu, Philip L.H. & Zhou, Xuan & Wang, Chao & Lo, P.H., 2011. "Modeling threshold conditional heteroscedasticity with regime-dependent skewness and kurtosis," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2590-2604, September.

   Cited by:

   1. Ke Zhu & Wai Keung Li, 2015. "A New Pearson-Type QMLE for Conditionally Heteroscedastic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 552-565, October.
      • Zhu, Ke & Li, Wai Keung, 2014. "A new Pearson-type QMLE for conditionally heteroskedastic models," MPRA Paper 52732, University Library of Munich, Germany.
      • Zhu, Ke & Li, Wai Keung, 2013. "A new Pearson-type QMLE for conditionally heteroskedastic models," MPRA Paper 52344, University Library of Munich, Germany.

4. Xixin Cheng & Philip L.H. Yu & W.K. Li, 2011. "Basket trading under co-integration with the logistic mixture autoregressive model," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1407-1419, July.

   Cited by:

   1. Law, K.F. & Li, W.K. & Yu, Philip L.H., 2018. "A single-stage approach for cointegration-based pairs trading," Finance Research Letters, Elsevier, vol. 26(C), pages 177-184.
   2. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
   3. Jordan Mann & J. Nathan Kutz, 2016. "Dynamic mode decomposition for financial trading strategies," Quantitative Finance, Taylor & Francis Journals, vol. 16(11), pages 1643-1655, November.
   4. Yu, Philip L.H. & Lu, Renjie, 2017. "Cointegrated market-neutral strategy for basket trading," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 112-124.

5. Lee, Paul H. & Yu, Philip L.H., 2010. "Distance-based tree models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1672-1682, June.

   Cited by:

   1. Antonella Plaia & Mariangela Sciandra, 2019. "Weighted distance-based trees for ranking data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 427-444, June.
   2. Lee, Paul H. & Yu, Philip L.H., 2012. "Mixtures of weighted distance-based models for ranking data with applications in political studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2486-2500.
   3. Yu-Shan Shih & Kuang-Hsun Liu, 2019. "Regression trees for detecting preference patterns from rank data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 683-702, September.
   4. Kung, Yi-Hung & Lin, Chang-Ting & Shih, Yu-Shan, 2012. "Split variable selection for tree modeling on rank data," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2830-2836.
   5. Ghimire, Ramesh & Green, Gary T. & Paudel, Krishna P. & Poudyal, Neelam C. & Cordell, H. Ken, 2017. "Visitors' Preferences for Freshwater Amenity Characteristics: Implications from the U.S. Household Survey," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 42(1), pages 1-24, January.
   6. Jianbo Li & Minggao Gu & Tao Hu, 2012. "General partially linear varying-coefficient transformation models for ranking data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1475-1488, January.
   7. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.
   8. Antonio D’Ambrosio & Willem J. Heiser, 2016. "A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 774-794, September.
   9. Krishna Paudel & Mahesh Pandit & Michael Dunn, 2013. "Using spectral analysis and multinomial logit regression to explain households’ choice patterns," Empirical Economics, Springer, vol. 44(2), pages 739-760, April.
   10. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.

6. Lam, Kin & Yu, P.L.H. & Lee, P.H., 2010. "A margin scheme that advises on when to change required margin," European Journal of Operational Research, Elsevier, vol. 207(1), pages 524-530, November.

   Cited by:

   1. Tong, Jun & Hu, Jiaqiao & Hu, Jianqiang, 2017. "Computing equilibrium prices for a capital asset pricing model with heterogeneous beliefs and margin-requirement constraints," European Journal of Operational Research, Elsevier, vol. 256(1), pages 24-34.
   2. Shi, Ruoding & Isengildina Massa, Olga, 2018. "Double-Edged Sword: Liquidity Implications of Futures Hedging," 2018 Annual Meeting, August 5-7, Washington, D.C. 274106, Agricultural and Applied Economics Association.
   3. Alexander, Carol & Kaeck, Andreas & Sumawong, Anannit, 2019. "A parsimonious parametric model for generating margin requirements for futures," European Journal of Operational Research, Elsevier, vol. 273(1), pages 31-43.

7. Philip Yu & Wai Keung Li & Shusong Jin, 2010. "On Some Models for Value-At-Risk," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 622-641.

   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.
   2. Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," KIER Working Papers 775, Kyoto University, Institute of Economic Research.
      • Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
      • Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," Documentos de Trabajo del ICAE 2011-16, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
      • Chen, C.W.S. & Gerlach, R. & Hwang, B.B.K. & McAleer, M.J., 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range," Econometric Institute Research Papers EI 2011-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
      • Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," Working Papers in Economics 11/22, University of Canterbury, Department of Economics and Finance.
   3. Mateusz Buczyński & Marcin Chlebus, 2019. "Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states," Working Papers 2019-12, Faculty of Economic Sciences, University of Warsaw.
   4. Abad, Pilar & Benito, Sonia, 2013. "A detailed comparison of value at risk estimates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 258-276.
   5. Peng, Wei & Hu, Shichao & Chen, Wang & Zeng, Yu-feng & Yang, Lu, 2019. "Modeling the joint dynamic value at risk of the volatility index, oil price, and exchange rate," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 137-149.
   6. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
   7. Yuzhi Cai & Guodong Li, 2018. "A novel approach to modelling the distribution of financial returns," Working Papers 2018-22, Swansea University, School of Management.

8. Xixin Cheng & Philip L. H. Yu & W. K. Li, 2009. "On a dynamic mixture GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 247-265.

   Cited by:

   1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
   2. 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.
      • Markus Haas & Jochen Krause & Marc S. Paolella & Sven C. Steude, 2013. "Time-Varying Mixture GARCH Models and Asymmetric Volatility," Swiss Finance Institute Research Paper Series 13-04, Swiss Finance Institute.
   3. Klein, Tony & Walther, Thomas, 2016. "Oil price volatility forecast with mixture memory GARCH," Energy Economics, Elsevier, vol. 58(C), pages 46-58.

9. So, Mike K.P. & Yu, Philip L.H., 2006. "Empirical analysis of GARCH models in value at risk estimation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(2), pages 180-197, April.

   Cited by:

   1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
   2. Kulp-Tåg, Sofie, 2007. "An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions," Working Papers 526, Hanken School of Economics.
   3. Yin Liao, 2012. "Does Modeling Jumps Help? A Comparison of Realized Volatility Models for Risk Prediction," CAMA Working Papers 2012-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
   4. Su, Jung-Bin & Lee, Ming-Chih & Chiu, Chien-Liang, 2014. "Why does skewness and the fat-tail effect influence value-at-risk estimates? Evidence from alternative capital markets," International Review of Economics & Finance, Elsevier, vol. 31(C), pages 59-85.
   5. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
   6. Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
   7. Chrétien, Stéphane & Coggins, Frank, 2010. "Performance and conservatism of monthly FHS VaR: An international investigation," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 323-333, December.
   8. Liao, Yin, 2013. "The benefit of modeling jumps in realized volatility for risk prediction: Evidence from Chinese mainland stocks," Pacific-Basin Finance Journal, Elsevier, vol. 23(C), pages 25-48.
   9. Mei-Ling Tang & Trung K. Do, 2019. "In search of robust methods for multi-currency portfolio construction by value at risk," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(1), pages 107-126, March.
   10. Onder Buberkoku, 2018. "Examining the Value-at-risk Performance of Fractionally Integrated GARCH Models: Evidence from Energy Commodities," International Journal of Economics and Financial Issues, Econjournals, vol. 8(3), pages 36-50.
   11. Durán Santomil, Pablo & Otero González, Luís & Martorell Cunill, Onofre & Merigó Lindahl, José M., 2018. "Backtesting an equity risk model under Solvency II," Journal of Business Research, Elsevier, vol. 89(C), pages 216-222.
   12. Angelidis, Timotheos & Degiannakis, Stavros, 2007. "Backtesting VaR Models: A Τwo-Stage Procedure," MPRA Paper 96327, University Library of Munich, Germany.
       • Angelidis, Timotheos & Degiannakis, Stavros, 2007. "Backtesting VaR Models: A Τwo-Stage Procedure," MPRA Paper 80418, University Library of Munich, Germany.
   13. Cheng-Few Lee & Jung-Bin Su, 2012. "Alternative statistical distributions for estimating value-at-risk: theory and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 39(3), pages 309-331, October.
   14. Diamandis, Panayiotis F. & Drakos, Anastassios A. & Kouretas, Georgios P. & Zarangas, Leonidas, 2011. "Value-at-risk for long and short trading positions: Evidence from developed and emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 165-176, June.
   15. Cathy W. S. Chen & Mike K. P. So & Edward M. H. Lin, 2009. "Volatility forecasting with double Markov switching GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(8), pages 681-697.
   16. Laura Garcia-Jorcano & Alfonso Novales, 2019. "Volatility specifications versus probability distributions in VaR forecasting," Documentos de Trabajo del ICAE 2019-26, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
   17. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.
       • Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2007. "A Robust VaR Model under Different Time Periods and Weighting Schemes," MPRA Paper 80466, University Library of Munich, Germany.
   18. Sang Hoon Kang & Seong-Min Yoon, 2009. "Value-at-Risk Analysis for Asian Emerging Markets: Asymmetry and Fat Tails in Returns Innovation," Korean Economic Review, Korean Economic Association, vol. 25, pages 387-411.
   19. Bagher Adabi & Mohsen Mehrara & Shapour Mohammadi, 2015. "Evaluation Approaches of Value at Risk for Tehran Stock Exchange," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 19(1), pages 41-62, Winter.
   20. Chuang, Chung-Chu & Wang, Yi-Hsien & Yeh, Tsai-Jung & Chuang, Shuo-Li, 2014. "Backtesting VaR in consideration of the higher moments of the distribution for minimum-variance hedging portfolios," Economic Modelling, Elsevier, vol. 42(C), pages 15-19.
   21. Alexander, Carol & Sheedy, Elizabeth, 2008. "Developing a stress testing framework based on market risk models," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2220-2236, October.
   22. Timmy Elenjical & Patrick Mwangi & Barry Panulo & Chun-Sung Huang, 2016. "A comparative cross-regime analysis on the performance of GARCH-based value-at-risk models: Evidence from the Johannesburg stock exchange," Risk Management, Palgrave Macmillan, vol. 18(2), pages 89-110, August.
   23. Paul Bui Quang & Tony Klein & Nam H. Nguyen & Thomas Walther, 2018. "Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(2), pages 1-20, April.
   24. Nico Katzke & Chris Garbers, 2015. "Do Long Memory and Asymmetries Matter When Assessing Downside Return Risk?," Working Papers 06/2015, Stellenbosch University, Department of Economics.
   25. Chaker Aloui, 2015. "Volatility forecasting and risk management in some MENA stock markets: a nonlinear framework," Afro-Asian Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 5(2), pages 160-192.
   26. Tomáš Jeøábek, 2020. "The Efficiency of GARCH Models in Realizing Value at Risk Estimates," ACTA VSFS, University of Finance and Administration, vol. 14(1), pages 32-50.
   27. Pitera, Marcin & Schmidt, Thorsten, 2018. "Unbiased estimation of risk," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 133-145.
   28. Marcin Pitera & Thorsten Schmidt, 2016. "Unbiased estimation of risk," Papers 1603.02615, arXiv.org, revised Aug 2017.
   29. Siva Kiran GUPTHA. K & Prabhakar RAO. R, 2019. "GARCH based VaR estimation: An empirical evidence from BRICS stock markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(621), W), pages 201-218, Winter.
   30. McMillan, David G. & Kambouroudis, Dimos, 2009. "Are RiskMetrics forecasts good enough? Evidence from 31 stock markets," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 117-124, June.
   31. Su, Jung-Bin & Hung, Jui-Cheng, 2011. "Empirical analysis of jump dynamics, heavy-tails and skewness on value-at-risk estimation," Economic Modelling, Elsevier, vol. 28(3), pages 1117-1130, May.
   32. Raúl de Jesús-Gutiérrez & Roberto J. Santillán-Salgado, 2019. "Conditional Extreme Values Theory and Tail-related Risk Measures: Evidence from Latin American Stock Markets," International Journal of Economics and Financial Issues, Econjournals, vol. 9(3), pages 127-141.
   33. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
   34. Mabrouk, Samir & Saadi, Samir, 2012. "Parametric Value-at-Risk analysis: Evidence from stock indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(3), pages 305-321.
   35. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
   36. Dilip Kumar, 2016. "Estimating and forecasting value-at-risk using the unbiased extreme value volatility estimator," Proceedings of Economics and Finance Conferences 3205528, International Institute of Social and Economic Sciences.
   37. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.
   38. Victoria Gabriela ANGHELACHE & Dumitru Cristian OANEA & Bogdan ZUGRAVU, 2013. "General Aspects Regarding the Methodology for Prediction Risk," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(2), pages 66-72, May.
   39. Su, Jung-Bin, 2015. "Value-at-risk estimates of the stock indices in developed and emerging markets including the spillover effects of currency market," Economic Modelling, Elsevier, vol. 46(C), pages 204-224.

10. Philip L. H. Yu & K. F. Lam & S. M. Lo, 2005. "Factor analysis for ranked data with application to a job selection attitude survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 583-597, July.

    Cited by:

    1. Xu, Hang & Alvo, Mayer & Yu, Philip L.H., 2018. "Angle-based models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 113-136.
    2. Philip Yu & Paul Lee & W. Wan, 2013. "Factor analysis for paired ranked data with application on parent–child value orientation preference data," Computational Statistics, Springer, vol. 28(5), pages 1915-1945, October.

11. Li, W. K. & Yu, Philip L. H., 2003. "On the residual autocorrelation of the autoregressive conditional duration model," Economics Letters, Elsevier, vol. 79(2), pages 169-175, May.

    Cited by:

    1. Ng, F.C. & Li, W.K. & Yu, Philip L.H., 2016. "Diagnostic checking of the vector multiplicative error model," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 86-97.
    2. Maria Pacurar, 2008. "Autoregressive Conditional Duration Models In Finance: A Survey Of The Theoretical And Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 711-751, September.
    3. Patrick W Saart & Jiti Gao & Nam Hyun Kim, 2014. "Econometric Time Series Specification Testing in a Class of Multiplicative Error Models," Monash Econometrics and Business Statistics Working Papers 1/14, Monash University, Department of Econometrics and Business Statistics.
    4. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," CAEPR Working Papers 2007-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    5. Duchesne, Pierre, 2004. "On the asymptotic distribution of the residual autocovariance matrices in the autoregressive conditional multinomial model," Economics Letters, Elsevier, vol. 83(2), pages 193-197, May.
    6. Gao, Jiti & Kim, Nam Hyun & Saart, Patrick W., 2015. "A misspecification test for multiplicative error models of non-negative time series processes," Journal of Econometrics, Elsevier, vol. 189(2), pages 346-359.

12. Philip Yu & Yijun Sun & Bimal Sinha, 2002. "Estimation of the Common Mean of a Bivariate Normal Population," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(4), pages 861-878, December.

    Cited by:

    1. N. Balakrishnan & N. Martín & L. Pardo, 2017. "Empirical phi-divergence test statistics for the difference of means of two populations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(2), pages 199-226, April.
    2. Gupta, Ramesh C. & Li, Xue, 2006. "Statistical inference for the common mean of two log-normal distributions and some applications in reliability," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3141-3164, July.

13. Philip Yu, 2000. "Bayesian analysis of order-statistics models for ranking data," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 281-299, September.

    Cited by:

    1. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    2. Lee, Paul H. & Yu, Philip L.H., 2010. "Distance-based tree models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1672-1682, June.
    3. Amadou Sawadogo & Simplice Dossou-Gbété & Dominique Lafon, 2017. "Ties in one block comparison experiments: a generalization of the Mallows–Bradley–Terry ranking model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2621-2644, October.
    4. Wiltrud Kuhlisch & Magnus Roos & Jörg Rothe & Joachim Rudolph & Björn Scheuermann & Dietrich Stoyan, 2016. "A statistical approach to calibrating the scores of biased reviewers of scientific papers," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(1), pages 37-57, January.
    5. Xu, Hang & Alvo, Mayer & Yu, Philip L.H., 2018. "Angle-based models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 113-136.

14. P. L. H. Yu & K. Lam, 1997. "How to predict election winners from a poll," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(1), pages 11-24.

    Cited by:

    1. Paap, R. & van Nierop, J.E.M. & van Heerde, H.J. & Wedel, M. & Franses, Ph.H.B.F. & Alsem, K.J., 2000. "Consideration sets, intentions and the inclusion of "Don't know" in a two-stage model for voter choice," Econometric Institute Research Papers EI 2000-33/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
       • Paap, Richard & van Nierop, Erjen & van Heerde, Harald J. & Wedel, Michel & Franses, Philip Hans & Alsem, Karel Jan, 2005. "Consideration sets, intentions and the inclusion of "don't know" in a two-stage model for voter choice," International Journal of Forecasting, Elsevier, vol. 21(1), pages 53-71.

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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 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-MST: Market Microstructure (1) 2007-08-18

Corrections

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