IDEAS home Printed from https://ideas.repec.org/r/zbw/cofedp/9916.html
   My bibliography  Save this item

SEMIFAR Models - A Semiparametric Framework for Modelling Trends, Long Range Dependence and Nonstationarity

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Beran, Jan & Feng, Yuanhua, 1999. "Local Polynomial Estimation with a FARIMA-GARCH Error Process," CoFE Discussion Papers 99/08, University of Konstanz, Center of Finance and Econometrics (CoFE).
  2. Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2013. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 249-265, February.
  3. Beran, Jan & Feng, Yuanhua & Ghosh, Sucharita & Sibbertsen, Philipp, 2002. "On robust local polynomial estimation with long-memory errors," International Journal of Forecasting, Elsevier, vol. 18(2), pages 227-241.
  4. Yuanhua Feng & Jan Beran, 2013. "Optimal convergence rates in non-parametric regression with fractional time series errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 30-39, January.
  5. Beran, Jan & Feng, Yuanhua & Franke, Günter & Hess, Dieter & Ocker, Dirk, 1999. "SEMIFAR Models, with Applications to Commodities, Exchange Rates and the Volatility of Stock Market Indices," CoFE Discussion Papers 99/18, University of Konstanz, Center of Finance and Econometrics (CoFE).
  6. Feng, Yuanhua, 2002. "Modelling Different Volatility Components," CoFE Discussion Papers 02/18, University of Konstanz, Center of Finance and Econometrics (CoFE).
  7. Schröder, Michael & Lüders, Erik, 2004. "Modeling Asset Returns: A Comparison of Theoretical and Empirical Models," ZEW Discussion Papers 04-19 [rev.], ZEW - Leibniz Centre for European Economic Research.
  8. repec:ipg:wpaper:2014-066 is not listed on IDEAS
  9. Beran, Jan & Feng, Yuanhua, 2000. "Data-driven estimation of semiparametric fractional autoregressive models," CoFE Discussion Papers 00/16, University of Konstanz, Center of Finance and Econometrics (CoFE).
  10. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model," Working Papers 07-19, Association Française de Cliométrie (AFC).
  11. Beran, Jan & Ghosh, Sucharita & Sibbertsen, Philipp, 2000. "Nonparametric M-estimation with long-memory errors," Technical Reports 2000,36, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  12. Beran, Jan & Feng, Yuanhua & Heiler, Siegfried, 2000. "Modifying the double smoothing bandwidth selector in nonparametric regression," CoFE Discussion Papers 00/37, University of Konstanz, Center of Finance and Econometrics (CoFE).
  13. Rinke, Saskia & Busch, Marie & Leschinski, Christian, 2017. "Long Memory, Breaks, and Trends: On the Sources of Persistence in Inflation Rates," Hannover Economic Papers (HEP) dp-584, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  14. Feng, Yuanhua, 2004. "Simultaneously Modeling Conditional Heteroskedasticity And Scale Change," Econometric Theory, Cambridge University Press, vol. 20(3), pages 563-596, June.
  15. Heni Boubaker & Nadia Sghaier, 2014. "Semiparametric Generalized Long Memory Modelling of GCC Stock Market Returns: A Wavelet Approach," Working Papers 2014-66, Department of Research, Ipag Business School.
  16. Beran, Jan & Ocker, Dirk, 1999. "SEMIFAR Forecasts, with Applications to Foreign Exchange Rates," CoFE Discussion Papers 99/13, University of Konstanz, Center of Finance and Econometrics (CoFE).
  17. Morana, Claudio & Beltratti, Andrea, 2004. "Structural change and long-range dependence in volatility of exchange rates: either, neither or both?," Journal of Empirical Finance, Elsevier, vol. 11(5), pages 629-658, December.
  18. Claude Diebolt & Vivien Guiraud, 2005. "A Note On Long Memory Time Series," Quality & Quantity: International Journal of Methodology, Springer, vol. 39(6), pages 827-836, December.
  19. Beran, Jan & Feng, Yuanhua, 2002. "Recent Developments in Non- and Semiparametric Regression with Fractional Time Series Errors," CoFE Discussion Papers 02/13, University of Konstanz, Center of Finance and Econometrics (CoFE).
  20. Beran, Jan & Ocker, Dirk, 1999. "Volatility of Stock Market Indices - An Analysis based on SEMIFAR Models," CoFE Discussion Papers 99/14, University of Konstanz, Center of Finance and Econometrics (CoFE).
  21. Feng, Yuanhua, 2002. "An Iterative Plug-In Algorithm for Nonparametric Modelling of Seasonal Time Series," CoFE Discussion Papers 02/04, University of Konstanz, Center of Finance and Econometrics (CoFE).
  22. Feng, Yuanhua, 2003. "Kernel Dependent Functions in Nonparametric Regression with Fractional Time Series Errors," CoFE Discussion Papers 03/02, University of Konstanz, Center of Finance and Econometrics (CoFE).
  23. Boubaker, Heni & Sghaier, Nadia, 2015. "Semiparametric generalized long-memory modeling of some mena stock market returns: A wavelet approach," Economic Modelling, Elsevier, vol. 50(C), pages 254-265.
  24. Beran, Jan & Feng, Yuanhua, 2001. "Supplement to the Paper "Interative plug-in algorithms for SEMIFAR models - definition, convergence and asymptotic properties": Detailed Simulation Results," CoFE Discussion Papers 01/12, University of Konstanz, Center of Finance and Econometrics (CoFE).
  25. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "A Dual Generalized Long Memory Modelling for Forecasting Electricity Spot Price: Neural Network and Wavelet Estimate," Papers 2204.08289, arXiv.org.
  26. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting," Papers 2204.09568, arXiv.org.
  27. Beran, Jan & Feng, Yuanhua, 2001. "Iterative plug-in algorithms for SEMIFAR models - definition, convergence and asymptotic properties," CoFE Discussion Papers 01/11, University of Konstanz, Center of Finance and Econometrics (CoFE).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.