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Numerical evaluation of likelihood inferences in Beta-t-Skew-EGARCH models

Author

Listed:
  • Fernanda Maria Muller

    (Universidade Federal de Santa Maria)

  • Fábio Mariano Bayer

    (Universidade Federal de Santa Maria, Departamento de Estatística)

Abstract

The Beta-Skew-t-EGARCH model was recently proposed in literature to model the volatility of financial returns. The inferences over the parameters of the model are based on maximum likelihood method. These estimators have good asymptotic properties, however in finite sample sizes their performance can be poor. With the purpose of evaluating the finite sample performance of point estimators and of the likelihood ratio test proposed to the presence of two components of volatility, we present a Monte Carlo simulation study. Numerical results indicate that the maximum likelihood estimators of some parameters of the model are considerably biased in sample sizes smaller than 3000. The evaluation results of the proposed two-component test, in terms of size and power of the test, showed its practical usefulness in sample sizes greater than 3000. At the end of the work we present an application in a real data of the proposed two-component test and the model Beta-Skew-t-EGARCH.

Suggested Citation

  • Fernanda Maria Muller & Fábio Mariano Bayer, 2015. "Numerical evaluation of likelihood inferences in Beta-t-Skew-EGARCH models," Brazilian Review of Finance, Brazilian Society of Finance, vol. 13(1), pages 40-73.
    • Handle: RePEc:brf:journl:v:13:y:2015:i:1:p:40-73
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    More about this item

    Keywords

    Beta-Skew-t-EGARCH; maximum likelihood estimator; Monte Carlo simulation; likelihood ratio test; volatility;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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