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G@RCH 2.0: An Ox Package for Estimating and Forecasting Various ARCH Models

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  • S»bastien Laurent and Jean-Philippe Peters

Abstract

This paper discusses and documents G@RCH 2.0, an Ox package dedicated to the estimation and forecasting of various univariate ARCH-type models including the GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH, FIEGARCH and FIAPARCH specifications of the conditional variance and an AR(FI)MA specification of the conditional mean. These models can be estimated by approximate (quasi-) maximum likelihood under four assumptions: normal, Student-t, GED, or skewed Student errors. Explanatory variables can enter both the conditional-mean and the conditional-variance equations. One-step-ahead (density) forecasts of both the conditional mean and variance are available as well as some misspecification tests and several graphical techniques.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 123.

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Date of creation: 01 Apr 2001
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Handle: RePEc:sce:scecf1:123

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Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
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Related research

Keywords: (Density-) Forecasts; GARCH; Asymmetry; Long Memory; Ox; Econometric Software; Financial Time Series;

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Cited by:
  1. Helen Higgs & Andrew C. Worthington, 2005. "Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 23-42.
  2. Charles, Amelie & Darne, Olivier, 2006. "Large shocks and the September 11th terrorist attacks on international stock markets," Economic Modelling, Elsevier, vol. 23(4), pages 683-698, July.
  3. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
  4. Francisco Cribari-Neto & Spyros Zarkos, 2003. "Econometric and Statistical Computing Using Ox," Computational Economics, Society for Computational Economics, vol. 21(3), pages 277-295, June.
  5. Menelaos Karananos & S.H Sekioua & N Zeng, 2005. "On the order of integration of monthly US ex-ante and ex-post real interest rates new evidence from over a century of data," Money Macro and Finance (MMF) Research Group Conference 2005 21, Money Macro and Finance Research Group.
  6. Cifter, Atilla & Ozun, Alper, 2007. "The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey," MPRA Paper 2489, University Library of Munich, Germany.
  7. C. R. McKenzie & Sumiko Takaoka, 2007. "EViews 5.1," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1145-1152.
  8. Wolfgang Härdle & Julius Mungo, 2008. "Value-at-Risk and Expected Shortfall when there is long range dependence," SFB 649 Discussion Papers SFB649DP2008-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  9. Guneratne Banda Wickremasinghe & Param Silvapulle, 2004. "Role of Exchange Rate Volatility in Exchange Rate Pass-Through to Import Prices: Some Evidence from Japan," International Finance 0406006, EconWPA.
  10. Ole E. Barndorff-Nielsen & Silja Kinnebrock & Neil Shephard, 2008. "Measuring downside risk — realised semivariance," CREATES Research Papers 2008-42, School of Economics and Management, University of Aarhus.

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