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A non-nested test of GARCH vs. EGARCH models

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  • Jung-Hee Lee
  • B. Wade Brorsen

Abstract

This study uses a Cox-type non-nested test. The test is obtained using Monte Carlo hypothesis tests with the log likelihood ratio as the test statistic. Monte Carlo methods are used to obtain the probability of a larger value of the test statistic under the null hypothesis. The approach used does not rely upon asymptotic normality. Using the maximum likelihood estimation technique, two competing time series models, generalized autoregressive conditional heteroscedasticity (GARCH) and exponential GARCH (EGARCH) models of daily spot prices of Deutsche mark are estimated. Using Monte Carlo hypothesis tests, then, p-values for GARCH vs. EGARCH models are calculated. The EGARCH model cannot be rejected, while the GARCH model is rejected.

Suggested Citation

  • Jung-Hee Lee & B. Wade Brorsen, 1997. "A non-nested test of GARCH vs. EGARCH models," Applied Economics Letters, Taylor & Francis Journals, vol. 4(12), pages 765-768.
    • Handle: RePEc:taf:apeclt:v:4:y:1997:i:12:p:765-768
    DOI: 10.1080/758528724
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    Citations

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    Cited by:

    1. Jonathan Batten & Francis In, 2006. "Dynamic interaction and valuation of quality yen Eurobonds in a multivariate EGARCH framework," Applied Financial Economics, Taylor & Francis Journals, vol. 16(12), pages 881-892.
    2. Caporin, M. & McAleer, M.J., 2010. "Model Selection and Testing of Conditional and Stochastic Volatility Models," Econometric Institute Research Papers EI 2010-57, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Batten, Jonathan & Hogan, Warren & In, Francis, 2002. "Valuing Credit Spreads on Quality Australian Dollar Eurobonds in a Multivariate EGARCH Framework," Australian Economic Papers, Wiley Blackwell, vol. 41(1), pages 115-128, March.
    4. Malmsten, Hans, 2004. "Evaluating exponential GARCH models," SSE/EFI Working Paper Series in Economics and Finance 564, Stockholm School of Economics, revised 03 Sep 2004.
    5. Ahmed, Shamim & Valente, Giorgio, 2015. "Understanding the price of volatility risk in carry trades," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 118-129.
    6. M. Karanasos & J. Kim, 2003. "Moments of the ARMA--EGARCH model," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 146-166, June.
    7. Amare Wubishet Ayele & Emmanuel Gabreyohannes & Yohannes Yebabe Tesfay, 2017. "Macroeconomic Determinants of Volatility for the Gold Price in Ethiopia: The Application of GARCH and EWMA Volatility Models," Global Business Review, International Management Institute, vol. 18(2), pages 308-326, April.
    8. In, Francis & Brown, Rob & Fang, Victor, 2003. "Modeling volatility and changes in the swap spread," International Review of Financial Analysis, Elsevier, vol. 12(5), pages 545-561.
    9. Teräsvirta, Timo, 2006. "An introduction to univariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 646, Stockholm School of Economics.
    10. Malmsten, Hans & Teräsvirta, Timo, 2004. "Stylized Facts of Financial Time Series and Three Popular Models of Volatility," SSE/EFI Working Paper Series in Economics and Finance 563, Stockholm School of Economics, revised 03 Sep 2004.
    11. Jonathan Batten & Warren Hogan & Francis In, 2002. "Valuing Credit Spreads on Quality Australian Dollar Eurobonds in a Multivariate EGARCH Framework," Australian Economic Papers, Wiley Blackwell, vol. 41(1), pages 115-128, March.
    12. N. Coulibaly & B. Wade Brorsen, 1999. "Monte carlo sampling approach to testing nonnested hypothesis: monte carlo results," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 195-209.

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