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On The Accuracy of GARCH Estimation in R Packages

Author

Listed:
  • Chelsey Hill

    (Department of Decision Sciences & MIS, Drexel University)

  • B. D. McCullough

    (Department of Decision Sciences & MIS, Drexel University)

Abstract

The R software is commonly used in applied finance and generalized autoregressive conditionally heteroskedastic (GARCH) estimation is a staple of applied finance; many papers use R to compute GARCH estimates. While R offers three different packages that compute GARCH estimates, they are not equally accurate. We apply the FCP GARCH benchmark (Fiorentini, Calzolari and Panattoni, 1996), proposed by McCullough and Renfro (1999), which uses the Bollerslev and Ghysels (1996) daily returns data, on three R packages: fGarch, rugarch, and tseries.

Suggested Citation

  • Chelsey Hill & B. D. McCullough, 2019. "On The Accuracy of GARCH Estimation in R Packages," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 4(2), pages 133-156, December.
    • Handle: RePEc:sgh:erfinj:v:4:y:2019:i:2:p:133-156
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    References listed on IDEAS

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    1. Nash, John C., 2014. "On Best Practice Optimization Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i02).
    2. Fiorentini, Gabriele & Calzolari, Giorgio & Panattoni, Lorenzo, 1996. "Analytic Derivatives and the Computation of GARCH Estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(4), pages 399-417, July-Aug..
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. B. D. McCullough & H. D. Vinod, 2004. "Verifying the Solution from a Nonlinear Solver: A Case Study: Reply," American Economic Review, American Economic Association, vol. 94(1), pages 400-406, March.
    7. 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.
    8. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    9. 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.
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    Cited by:

    1. Yanting Chen & Peter R. Hartley & Yihui Lan, 2023. "Temperature, storage, and natural gas futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(4), pages 549-575, April.

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    More about this item

    Keywords

    algorithms; benchmark; software accuracy; GARCH;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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