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Where did the GARCH Models Perform Best in Terms of Volatility Forecasting? Equity vs. Commodities Markets

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  • Iulian Lolea

Abstract

This article aims to compare the performance of GARCH models in terms of volatility forecasting for two asset classes: equity and commodities. The idea behind this research was that GARCH models may perform differently depending on the asset class for which they are used. A comparison based on performance of GARCH, EGARCH and GJR-GARCH for the Romanian equity market, Polish equity market, gold market and Brent Crude Oil market has been done. The results were in line with initial expectations. Both, in-sample and out-of-sample analysis, highlighted the over performance of GARCH models for the equity market compared to the commodity market. Moreover, the performance of GARCH models in terms of volatility forecasting for the gold market decreases as the forecast horizon increases. Thus, it has been proved that there is a bias of classical GARCH models to perform better for equity markets, compared to commodity markets, but future researches can be directed towards adapting GARCH models to the commodity market. A possible solution would be to implement models that allow regime switching as the Markov Switching GARCH (MRS-GARCH) models do.

Suggested Citation

  • Iulian Lolea, 2017. "Where did the GARCH Models Perform Best in Terms of Volatility Forecasting? Equity vs. Commodities Markets," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 3(3), pages 79-86, September.
    • Handle: RePEc:khe:scajes:v:3:y:2017:i:3:p:79-86
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    References listed on IDEAS

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    3. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    4. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Volatility forecasting; commodities; equity markets; statistical loss functions; out-of-sample;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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