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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.

Suggested Citation

  • S»bastien Laurent and Jean-Philippe Peters, 2001. "G@RCH 2.0: An Ox Package for Estimating and Forecasting Various ARCH Models," Computing in Economics and Finance 2001 123, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:123
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    Cited by:

    1. C. R. McKenzie & Sumiko Takaoka, 2007. "EViews 5.1," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1145-1152.
    2. repec:spr:jecfin:v:41:y:2017:i:4:d:10.1007_s12197-017-9386-x is not listed on IDEAS
    3. Ole E. Barndorff-Nielsen & Silja Kinnebrock & Neil Shephard, 2008. "Measuring downside risk — realised semivariance," CREATES Research Papers 2008-42, Department of Economics and Business Economics, Aarhus University.
    4. Atilla Çifter & Alper Özün, 2007. "The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 1(1), pages 7-34.
    5. Karanasos, M. & Sekioua, S.H. & Zeng, N., 2006. "On the order of integration of monthly US ex-ante and ex-post real interest rates: New evidence from over a century of data," Economics Letters, Elsevier, vol. 90(2), pages 163-169, February.
    6. 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.
    7. 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.
    8. 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.
    9. Eskandar A. Tooma, 2003. "Modeling and Forecasting Egyptian Stock Market Volatility Before and After Price Limits," Working Papers 0310, Economic Research Forum, revised 04 Mar 2003.
    10. 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.
    11. 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.
    12. Wang, Yuanfang & Roberts, Matthew C., 2005. "Realized Volatility in the Agricultural Futures Market," 2005 Annual meeting, July 24-27, Providence, RI 19211, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

    More about this item

    Keywords

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

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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