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Assessing Volatility Forecasting Models: Why GARCH Models Take the Lead

Author

Listed:
  • Matei, Marius

    (Ph.D. Student at ESADE Business School, Department of Finance, Barcelona and at National Institute of Economic Research, Romanian Academy, Bucharest)

Abstract

The paper provides a critical assessment of the main forecasting techniques and an evaluation of the superiority of the more advanced and complex models. Ultimately, its scope is to offer support for the rationale behind of an idea: GARCH is the most appropriate model to use when one has to evaluate the volatility of the returns of groups of stocks with large amounts (thousands) of observations. The appropriateness of the model is seen through a unidirectional perspective of the quality of volatility forecast provided by GARCH when compared to any other alternative model, without considering any cost component.

Suggested Citation

  • Matei, Marius, 2009. "Assessing Volatility Forecasting Models: Why GARCH Models Take the Lead," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 42-65, December.
    • Handle: RePEc:rjr:romjef:v::y:2009:i:4:p:42-65
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    Citations

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    Cited by:

    1. Dhanya Jothimani & Ravi Shankar & Surendra S. Yadav, 2016. "Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index," Papers 1605.07278, arXiv.org.
    2. Lin, Xiaoqiang & Fei, Fangyu, 2013. "Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis," Economic Modelling, Elsevier, vol. 31(C), pages 265-275.
    3. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
    4. Ahmad Muslim, 2014. "Analyzing volatility of rice price in Indonesia using ARCH/GARCH model," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 6(1), pages 1-12, April.
    5. Subashini Maniam & Chin Lee, 2018. "Stock Market Liberalization Impact on Sectoral Stock Market Return in Malaysia," Capital Markets Review, Malaysian Finance Association, vol. 26(2), pages 21-31.
    6. Tristan Nguyen & Thi Thanh Mai Bui, 2018. "Modeling the Volatility and Forecasting the Stock Price of the German Stock Index (DAX30)," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 4(4), pages 72-92, 04-2018.
    7. Nieto, María Rosa & Carmona-Benítez, Rafael Bernardo, 2018. "ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 1-8.
    8. Abokyi, Emmanuel & Asiedu, Kofi Fred, 2021. "Agricultural policy and commodity price stabilisation in Ghana: The role of buffer stockholding operations," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 16(4), December.
    9. Krzysztof DRACHAL, 2015. "The Structural Stability of a One-Day Risk Premium in View of the Recent Financial Crisis," Expert Journal of Economics, Sprint Investify, vol. 3(2), pages 136-142.
    10. Charalampos Basdekis & Apostolos Christopoulos & Alexandros Gkolfinopoulos & Ioannis Katsampoxakis, 2022. "VaR as a risk management framework for the spot and futures tanker markets," Operational Research, Springer, vol. 22(4), pages 4287-4352, September.
    11. Muhammad Ahsanuddin & Tayyab Raza Fraz & Samreen Fatima, 2019. "Studying the Volatility of Pakistan Stock Exchange and Shanghai Stock Exchange Markets in the Light of CPEC: An Application of GARCH and EGARCH Modelling," International Journal of Sciences, Office ijSciences, vol. 8(03), pages 125-132, March.
    12. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).

    More about this item

    Keywords

    volatility; GARCH; forecast; correlation; risk; heteroskedasticity;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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