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Testing for EGARCH Against Stochastic Volatility Models

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  • Masahito Kobayashi
  • Xiuhong Shi

Abstract

. It is shown that the EGARCH model is the degenerate case of Danielsson's [Journal of Econometrics (1994) Vol. 61, pp. 375–400] stochastic volatility model where the disturbance of the transition equation of conditional volatility has zero variance. The Lagrange multiplier test statistic is obtained for the EGARCH model against the stochastic volatility model by expressing the degenerate density under the null hypothesis by the Dirac delta function. The finite sample performance of the test is studied in a small Monte Carlo experiment.

Suggested Citation

  • Masahito Kobayashi & Xiuhong Shi, 2005. "Testing for EGARCH Against Stochastic Volatility Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 135-150, January.
    • Handle: RePEc:bla:jtsera:v:26:y:2005:i:1:p:135-150
    DOI: 10.1111/j.1467-9892.2005.00394.x
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    References listed on IDEAS

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    Cited by:

    1. Kobayashi, Masahito, 2009. "Testing for jumps in the stochastic volatility models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2597-2608.
    2. William C. Horrace & Ian A. Wright, 2020. "Stationary Points for Parametric Stochastic Frontier Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 516-526, July.
    3. Lama, A. & Jha, G.K. & Paul, R.K. & Gurung, B., 2015. "Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 28(1).
    4. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. 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.
    6. 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.
    7. Shi, Xiuhong & Kobayashi, Masahito, 2009. "Testing for jumps in the EGARCH process," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2797-2808.
    8. Allen, David E. & Gao, Jiti & McAleer, Michael, 2009. "Modelling and managing financial risk: An overview," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2521-2524.
    9. Daisuke Nagakura, 2008. "A note on the relationship between the information matrx test and a score test for parameter constancy," Economics Bulletin, AccessEcon, vol. 3(5), pages 1-7.
    10. repec:ebl:ecbull:v:3:y:2008:i:5:p:1-7 is not listed on IDEAS

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