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GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics

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Author Info
Robert Engle

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Abstract

ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial applications. These models are especially useful when the goal of the study is to analyze and forecast volatility. This paper gives the motivation behind the simplest GARCH model and illustrates its usefulness in examining portfolio risk. Extensions are briefly discussed.

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Publisher Info
Article provided by American Economic Association in its journal Journal of Economic Perspectives.

Volume (Year): 15 (2001)
Issue (Month): 4 (Fall)
Pages: 157-168
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Handle: RePEc:aea:jecper:v:15:y:2001:i:4:p:157-168

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Tim Bollerslev & Jeffrey Wooldridge, 1992. "Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances," Econometric Reviews, Taylor and Francis Journals, vol. 11(2), pages 143-172. [Downloadable!] (restricted)
  2. Tim Bollerslev & Jeffrey M. Wooldridge, 1988. "Quasi-Maximum Likelihood Estimation of Dynamic Models with Time-Varying Covariances," Working papers 505, Massachusetts Institute of Technology (MIT), Department of Economics.
  3. Ng, Victor & Engle, Robert F. & Rothschild, Michael, 1992. "A multi-dynamic-factor model for stock returns," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 245-266. [Downloadable!] (restricted)
  4. Engle, Robert F & Ito, Takatoshi & Lin, Wen-Ling, 1990. "Meteor Showers or Heat Waves? Heteroskedastic Intra-daily Volatility in the Foreign Exchange Market," Econometrica, Econometric Society, vol. 58(3), pages 525-42, May. [Downloadable!] (restricted)
    Other versions:
  5. Robert F. Engle & Gary G.J. Lee, 1993. "A Permanent and Transitory Component Model of Stock Return Volatility," University of California at San Diego, Economics Working Paper Series 92-44r, Department of Economics, UC San Diego. [Downloadable!]
  6. Robert Engle & Simone Manganelli, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series 1999-20, Department of Economics, UC San Diego. [Downloadable!]
    Other versions:
  7. Robert F. Engle & Victor K. Ng, 1991. "Measuring and Testing the Impact of News on Volatility," NBER Working Papers 3681, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  9. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December. [Downloadable!] (restricted)
    Other versions:
  10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. David Peel & Ivan Paya & E Pavlidis, 2009. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Working Papers 005913, Lancaster University Management School, Economics Department. [Downloadable!]
  2. Oberndorfer, Ulrich & Ulbricht, Dirk, 2007. "Lost in Transmission? Stock Market Impacts of the 2006 European Gas Crisis," ZEW Discussion Papers 07-030, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research. [Downloadable!]
  3. Viviana Fernández, 2003. "Extreme Value Theory: Value at Risk and Returns Dependence Around the World," Documentos de Trabajo 161, Centro de Economía Aplicada, Universidad de Chile. [Downloadable!]
  4. Yu Hsing, 2004. "Impacts Of Macroeconomic Policies On Output In The Czech Republic: An Application Of Romer'S Is-Mp-Ia Model," Prague Economic Papers, University of Economics, Prague, vol. 2004(4), pages 339-345. [Downloadable!] (restricted)
  5. Drew Creal & Siem Jan Koopman & Andre Lucas, 2009. "A General Framework for Observation Driven Time-Varying Parameter Models," Global COE Hi-Stat Discussion Paper Series gd08-038, Institute of Economic Research, Hitotsubashi University. [Downloadable!]
    Other versions:
  6. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," CFS Working Paper Series 2005/02, Center for Financial Studies. [Downloadable!]
    Other versions:
  7. Viviana Fernandez, 2003. "Extreme Value Theory and Value at Risk," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Economics Department, vol. 18(1), pages 57-85, June. [Downloadable!]
    Other versions:
  8. Söderberg, Jonas, 2008. "Test of the Gaussian Copula on the Swedish Stock Market," CAFO Working Papers 2009:9, Centre for Labour Market Policy Research (CAFO), School of Management and Economics, Växjö University. [Downloadable!]
  9. Walid Abdmoulah, . "Testing the Evolving Efficiency of 11 Arab Stock Markets," API-Working Paper Series 0907, Arab Planning Institute - Kuwait, Information Center. [Downloadable!]
  10. Humavindu, Michael N, 2008. "Essays on the Namibian Economy," UmeÃ¥ Economic Studies 745, Umeå University, Department of Economics. [Downloadable!]
  11. Yu Hsing, 2005. "Effects Of Macroeconomic Policies And Stock Market Performance On The Estonian Economy," Prague Economic Papers, University of Economics, Prague, vol. 2005(2), pages 109-116. [Downloadable!] (restricted)
  12. Steven Beach & Alexei Orlov, 2007. "An application of the Black–Litterman model with EGARCH-M-derived views for international portfolio management," Financial Markets and Portfolio Management, Springer, vol. 21(2), pages 147-166, June. [Downloadable!] (restricted)
  13. Jordaan, H. & Grove, B. & Jooste, A. & Alemu, A.G., 2007. "Measuring the Price Volatility of Certain Field Crops in South Africa using the ARCH/GARCH Approach," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 46(3), September. [Downloadable!]
  14. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2007. "Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation," MPRA Paper 3963, University Library of Munich, Germany. [Downloadable!]
  15. Siddiqi, Hammad, 2007. "Rational Interacting Agents and Volatility Clustering: A New Approach," MPRA Paper 2984, University Library of Munich, Germany. [Downloadable!]
  16. Yu Hsing, 2005. "Application of the IS-MP-IA model to the German economy and policy implications," Economics Bulletin, Economics Bulletin, vol. 15(5), pages 1-10. [Downloadable!]
  17. Valadkhani, Abbas & Chancharat, Surachai & Harvie, Charles, 2006. "The Interplay Between the Thai and Several Other International Stock Markets," Economics Working Papers wp06-18, School of Economics, University of Wollongong, NSW, Australia. [Downloadable!]
  18. Zhang, Zibin & Wetzstein, Michael, 2008. "New relationships: ethanol, corn, and gasoline volatility," Transition to a Bio Economy Conferences, Risk, Infrastructure and Industry Evolution Conference, June 24-25, 2008, Berkeley, California 48718, Farm Foundation. [Downloadable!]
  19. Siddiqi, Hammad, 2006. "Belief merging and revision under social influence: An explanation for the volatility clustering puzzle," MPRA Paper 657, University Library of Munich, Germany. [Downloadable!]
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