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Model-free versus Model-based Volatility Prediction

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  • Dimitris N. Politis

Abstract

The well-known ARCH/GARCH models for financial time series have been criticized of late for their poor performance in volatility prediction, that is, prediction of squared returns.-super-1 Focusing on three representative data series, namely a foreign exchange series (Yen vs. Dollar), a stock index series (the S&P500 index), and a stock price series (IBM), the case is made that financial returns may not possess a finite fourth moment. Taking this into account, we show how and why ARCH/GARCH models—when properly applied and evaluated—actually do have nontrivial predictive validity for volatility. Furthermore, we show how a simple model-free variation on the ARCH theme can perform even better in that respect. The model-free approach is based on a novel normalizing and variance-stabilizing transformation (NoVaS, for short) that can be seen as an alternative to parametric modeling. Properties of this transformation are discussed, and practical algorithms for optimizing it are given. Copyright , Oxford University Press.

Suggested Citation

  • Dimitris N. Politis, 0. "Model-free versus Model-based Volatility Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 358-359.
    • Handle: RePEc:oup:jfinec:v:5:y::i:3:p:358-359
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbm004
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    Cited by:

    1. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    2. Breitung, Jörg & Hafner, Christian M., 2016. "A simple model for now-casting volatility series," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1247-1255.
    3. Ekaterina Smetanina, 2017. "Real-Time GARCH," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 561-601.
    4. Francq, Christian & Zakoïan, Jean-Michel, 2022. "Testing the existence of moments for GARCH processes," Journal of Econometrics, Elsevier, vol. 227(1), pages 47-64.
    5. Panagiotis Mantalos, 2017. "Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1274282-127, January.
    6. Ding, Y., 2020. "Diffusion Limits of Real-Time GARCH," Cambridge Working Papers in Economics 20112, Faculty of Economics, University of Cambridge.
    7. Jie Chen & Dimitris N. Politis, 2019. "Optimal Multi-Step-Ahead Prediction of ARCH/GARCH Models and NoVaS Transformation," Econometrics, MDPI, vol. 7(3), pages 1-23, August.

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