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GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la volatilidad del mercado de valores marroquí?

Author

Listed:
  • El Jebari, Ouael

    (Department of Economics and Management, Faculty of Legal, Economical and Social Sciences – Ain Sebaa, University Hassan II, Casablanca (Morocco))

  • Hakmaoui, Abdelati

    (Department of Economics and Management, Faculty of Legal, Economical and Social Sciences – Ain Sebaa, University Hassan II, Casablanca (Morocco))

Abstract

Nowadays, modeling and forecasting the volatility of stock markets have become central to the practice of risk management; they have become one of the major topics in financial econometrics and they are principally and continuously used in the pricing of financial assets and the Value at Risk, as well as the pricing of options and derivatives. The aim of this article is to compare the GARCH (Generalised Auto Regressive Conditional Heteroskedasticity) family models —GARCH (1.1), GJR-GARCH, PGARCH, EGARCH, and IGARCH— with the EWMA (Exponentially Weighed Moving Average) model in the hope of finding the best model to forecast the volatility of the Moroccan stock-market index MADEX. We use daily returns covering the period between 01/04/1993 and 30/08/2016. We find that the asymmetric model IGARCH following a normal error distribution yields the best forecasting performance results and therefore, surpasses the EWMA model. Our results could have application in the risk management in Morocco, as well as leading to a better understanding of the Moroccan stock-exchange volatility dynamics, especially with the lack of previous similar studies. || Hoy en día, modelar y pronosticar la volatilidad de los mercados bursátiles se ha convertido en un aspecto central para la práctica de la gestión de riesgos; se ha convertido en uno de los temas principales en la econometría financiera y se utiliza principal y continuamente en la determinación de precios de los activos financieros y el valor en riesgo, así como la fijación de precios de opciones y derivados. El objetivo de este artículo es comparar los modelos de la familia GARCH (heterocedasticidad condicional regresiva automática generalizada) —GARCH (1.1), GJR-GARCH, PGARCH, EGARCH e IGARCH— con el modelo EWMA (media móvil ponderada exponencialmente) con la esperanza de encontrar el mejor modelo para pronosticar la volatilidad del índice bursátil marroquí MADEX. Utilizamos los rendimientos diarios que cubren el período comprendido entre el 01/04/1993 y el 30/08/2016. Encontramos que el modelo asimétrico IGARCH, siguiendo una distribución normal del error, produce los mejores resultados de pronóstico y, por lo tanto, supera al modelo EWMA. Nuestros resultados podrían tener una aplicación en la gestión de riesgos en Marruecos, así como llevar a una mejor comprensión de la dinámica de volatilidad de la bolsa de Marruecos, especialmente con la falta de estudios similares anteriores.

Suggested Citation

  • El Jebari, Ouael & Hakmaoui, Abdelati, 2018. "GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 237-249, Diciembre.
    • Handle: RePEc:pab:rmcpee:v:26:y:2018:i:1:p:237-249
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    References listed on IDEAS

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    More about this item

    Keywords

    volatility forecasting; volatility modeling; stylized facts; GARCH family models; EWMA; pronósticos de volatilidad; modelización de volatilidad; hechos estilizados; modelos de la familia GARCH; EWMA;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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