Yue Fang

Personal Details

First Name:	Yue
Middle Name:
Last Name:	Fang
Suffix:
RePEc Short-ID:	pfa44
	http://darkwing.uoregon.edu/~yfang/
	Lundquist College of Business University of Oregon Eugene, OR 97403
	541 346 3265

Affiliation

Charles H. Lundquist College of Business
University of Oregon

Eugene, Oregon (United States)
http://lcb.uoregon.edu/
RePEc:edi:coborus (more details at EDIRC)

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. Yue Fang & Sergio G. Koreisha, 2004. "Updating ARMA predictions for temporal aggregates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 275-296.

   Cited by:

   1. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
      • SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
   2. SILVESTRINI, Andrea & SALTo, Matteo & MOULIN, Laurent & VEREDAS, David, 2009. "Monitoring and forecasting annual public deficit every month: the case of France," LIDAM Reprints CORE 2019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Andrea Silvestrini & Matteo Salto & Laurent Moulin & David Veredas, 2008. "Monitoring and forecasting annual public deficit every month: the case of France," Empirical Economics, Springer, vol. 34(3), pages 493-524, June.
   3. Pena-Levano, Luis M. & Ramirez, Octavio & Renteria-Pinon, Mario, 2015. "Efficiency Gains in Commodity Forecasting with High Volatility in Prices using Different Levels of Data Aggregation," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205740, Agricultural and Applied Economics Association.
   4. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
   5. Pena-Levano, Luis M & Foster, Kenneth, 2016. "Efficiency gains in commodity forecasting using disaggregated levels versus more aggregated predictions," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235792, Agricultural and Applied Economics Association.
   6. Ramirez, Octavio A., 2012. "Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 123470, Agricultural and Applied Economics Association.
      • Ramirez, Octavio A., 2011. "Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts," Faculty Series 113520, University of Georgia, Department of Agricultural and Applied Economics.
   7. Garcia-Ferrer, A. & de Juan, A. & Poncela, P., 2006. "Forecasting traffic accidents using disaggregated data," International Journal of Forecasting, Elsevier, vol. 22(2), pages 203-222.
   8. Alexandre Petkovic, 2009. "Three essays on exotic option pricing, multivariate Lévy processes and linear aggregation of panel models," ULB Institutional Repository 2013/210357, ULB -- Universite Libre de Bruxelles.

2. Fang, Yue & Xu, Daming, 2003. "The predictability of asset returns: an approach combining technical analysis and time series forecasts," International Journal of Forecasting, Elsevier, vol. 19(3), pages 369-385.

   Cited by:

   1. David McMillan & Alan Speight, 2006. "Non-linear long horizon returns predictability: evidence from six south-east Asian markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 13(2), pages 95-111, June.
   2. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators," Papers 1907.09452, arXiv.org.
   3. Alexandros E. Milionis & Evangelia Papanagiotou, 2008. "A Note on the Use of Moving Average Trading Rules to Test For Weak from Efficiency in Capital Markets," Working Papers 91, Bank of Greece.
   4. Matthieu Garcin & Clément Goulet, 2017. "Non-parametric news impact curve: a variational approach," Post-Print halshs-01244292, HAL.
   5. Aatola, Piia & Ollikka, Kimmo & Ollikainen, Markku, 2012. "Informational Efficiency of the EU ETS market – a study of price predictability and profitable trading," Working Papers 28, VATT Institute for Economic Research.
   6. Wong, Wing-Keung & McAleer, Michael, 2009. "Mapping the Presidential Election Cycle in US stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(11), pages 3267-3277.
   7. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Mid-price prediction based on machine learning methods with technical and quantitative indicators," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-39, June.
   8. Alexandros E. Milionis & Evangelia Papanagiotou, 2008. "On the Use of the Moving Average Trading Rule to Test for Weak Form Efficiency in Capital Markets," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 37(2), pages 181-201, July.
   9. Matthieu Garcin & Clément Goulet, 2015. "A fully non-parametric heteroskedastic model," Documents de travail du Centre d'Economie de la Sorbonne 15086, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
   10. Lin Liu & Qiguang Chen, 2020. "How to compare market efficiency? The Sharpe ratio based on the ARMA-GARCH forecast," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-21, December.
   11. Matthieu Garcin & Clément Goulet, 2017. "Non-parametric news impact curve: a variational approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01244292, HAL.
   12. Alexandros E. Milionis & Evangelia Papanagiotou, 2011. "Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non linear dependencies in stock returns," Working Papers 134, Bank of Greece.
       • Alexandros E. Milionis & Evangelia Papanagiotou, 2013. "Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non-linear dependencies in stock returns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(11), pages 2480-2494, November.
   13. Giuseppe Galloppo, 2009. "Dynamic Asset Allocation Using a Combined Criteria Decision System," Accounting & Taxation, The Institute for Business and Finance Research, vol. 1(1), pages 29-44.
   14. McMillan, David G., 2007. "Non-linear forecasting of stock returns: Does volume help?," International Journal of Forecasting, Elsevier, vol. 23(1), pages 115-126.
   15. Qing Zhou & Robert Faff, 2017. "The complementary role of cross-sectional and time-series information in forecasting stock returns," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 113-139, February.
   16. Bartoš, Erik & Pinčák, Richard, 2017. "Identification of market trends with string and D2-brane maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 57-70.
   17. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.
   18. Tabak, Benjamin M. & Lima, Eduardo J.A., 2009. "Market efficiency of Brazilian exchange rate: Evidence from variance ratio statistics and technical trading rules," European Journal of Operational Research, Elsevier, vol. 194(3), pages 814-820, May.

3. Fang, Yue, 2003. "Forecasting combination and encompassing tests," International Journal of Forecasting, Elsevier, vol. 19(1), pages 87-94.

   Cited by:

   1. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
   2. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
   3. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2009. "Do Composite Procedures Really Improve the Accuracy of Outlook Forecasts?," 2009 Conference, April 20-21, 2009, St. Louis, Missouri 53052, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
   4. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
      • Rómulo Chumacero E., 2004. "Forecasting Chilean Industrial Production and Sales With Automated Procedures," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(3), pages 47-56, December.
      • Rómulo Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Working Papers Central Bank of Chile 260, Central Bank of Chile.
   5. Bedri Kamil Onur Taş, 2016. "Does the Federal Reserve have Private Information about its Future Actions?," Economica, London School of Economics and Political Science, vol. 83(331), pages 498-517, July.
   6. Ali Babikir & Henry Mwambi, 2016. "Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods," Empirical Economics, Springer, vol. 51(4), pages 1541-1556, December.
   7. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
   8. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
   9. Guillermo Benavides Perales, 2009. "Price volatility forecasts for agricultural commodities: an application of volatility models, option implieds and composite approaches forfutures prices of corn and wheat," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 3(2), pages 40-59.
   10. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
   11. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
   12. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip & Etienne, Xiaoli, 2012. "Composite and Outlook Forecast Accuracy," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-19, August.
   13. Antonis Michis, 2012. "Monitoring Forecasting Combinations with Semiparametric Regression Models," Working Papers 2012-2, Central Bank of Cyprus.
   14. Benavides Guillermo, 2006. "Volatility Forecasts for the Mexican Peso - U.S. Dollar Exchange Rate: An Empirical Analysis of Garch, Option Implied and Composite Forecast Models," Working Papers 2006-04, Banco de México.
   15. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
       • Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
   16. Viviana Fernández, 2006. "Forecasting crude oil and natural gas spot prices by classification methods," Documentos de Trabajo 229, Centro de Economía Aplicada, Universidad de Chile.
   17. Trapero, Juan R. & Pedregal, Diego J. & Fildes, R. & Kourentzes, N., 2013. "Analysis of judgmental adjustments in the presence of promotions," International Journal of Forecasting, Elsevier, vol. 29(2), pages 234-243.
   18. Bacci, Livio Agnew & Mello, Luiz Gustavo & Incerti, Taynara & Paulo de Paiva, Anderson & Balestrassi, Pedro Paulo, 2019. "Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated fact," International Journal of Production Economics, Elsevier, vol. 212(C), pages 186-211.
   19. Ruth, Karsten, 2008. "Macroeconomic forecasting in the EMU: Does disaggregate modeling improve forecast accuracy?," Journal of Policy Modeling, Elsevier, vol. 30(3), pages 417-429.
   20. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt’s exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759.
   21. Trapero, Juan R. & Kourentzes, N. & Fildes, R., 2012. "Impact of information exchange on supplier forecasting performance," Omega, Elsevier, vol. 40(6), pages 738-747.
   22. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
   23. Viviana Fernandez, 2008. "Traditional versus novel forecasting techniques: how much do we gain?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 637-648.
   24. Fildes, Robert & Kourentzes, Nikolaos, 2011. "Validation and forecasting accuracy in models of climate change," International Journal of Forecasting, Elsevier, vol. 27(4), pages 968-995, October.
   25. Xu, Ningzhe & Nie, Qifan & Liu, Jun & Jones, Steven, 2024. "Linking short- and long-term impacts of the COVID-19 pandemic on travel behavior and travel preferences in Alabama: A machine learning-supported path analysis," Transport Policy, Elsevier, vol. 151(C), pages 46-62.
   26. Fernandez, Viviana, 2007. "Wavelet- and SVM-based forecasts: An analysis of the U.S. metal and materials manufacturing industry," Resources Policy, Elsevier, vol. 32(1-2), pages 80-89.
   27. Petar Sorić & Ivana Lolić, 2015. "A note on forecasting euro area inflation: leave- $$h$$ h -out cross validation combination as an alternative to model selection," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(1), pages 205-214, March.
   28. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
   29. Rosa, Franco & Vasciaveo, Michela, 2012. "Volatility in US and Italian agricultural markets, interactions and policy evaluation," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122530, European Association of Agricultural Economists.
   30. Luis Fernando Melo & Héctor Núñez, 2004. "Combinación de Pronósticos de la Inflación en Presencia de cambios Estructurales," Borradores de Economia 286, Banco de la Republica de Colombia.
       • Luis Fernando Melo Velandia & Héctor M. Núñez Amortegui, 2004. "Combinación de pronósticos de la inflación en presencia de cambios estructurales," Borradores de Economia 2153, Banco de la Republica.
   31. Benavides, Guillermo, 2009. "Predictive Accuracy of Futures Options Implied Volatility: the Case of the Exchange Rate Futures Mexican Peso-Us Dollar," Panorama Económico, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(09), pages 55-95, segundo s.
   32. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt's exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759, July.
   33. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.

4. Yue Fang, 2000. "Seasonality in foreign exchange volatility," Applied Economics, Taylor & Francis Journals, vol. 32(6), pages 697-703.

   Cited by:

   1. Bernardo Quintanilla García & Jesús Cuauhtémoc Téllez Gaytán & Lawrence A. Wolfskill, 2012. "The Role Of Technical Analysis In The Foreign Exchange Market," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 6(3), pages 17-22.

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