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Answering the Critics: Yes, ARCH Models Do Provide Good Volatility Forecasts

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  • Torben G. Andersen
  • Tim Bollerslev

Abstract

Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good forecasts of future volatility are critical for the implementation and evaluation of asset pricing theories. In response to this, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility at the daily and lower frequencies using ARCH and stochastic volatility type models. Most of these studies find highly significant in-sample parameter estimates and pronounced intertemporal volatility persistence. Meanwhile, when judged by standard forecast evaluation criteria, based on the squared or absolute returns over daily or longer forecast horizons, ARCH models provide seemingly poor volatility forecasts. The present paper demonstrates that ARCH models, contrary to the above contention, produce strikingly accurate interdaily forecasts for the latent volatility factor that is relevant for most financial applications.

Suggested Citation

  • Torben G. Andersen & Tim Bollerslev, 1997. "Answering the Critics: Yes, ARCH Models Do Provide Good Volatility Forecasts," NBER Working Papers 6023, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:6023
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    Cited by:

    1. Klaassen, F.J.G.M., 1998. "Improving Garch Volatility Forecasts," Discussion Paper 1998-52, Tilburg University, Center for Economic Research.
    2. Benoit Perron, 2003. "Semiparametric Weak-Instrument Regressions with an Application to the Risk-Return Tradeoff," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 424-443, May.
    3. Capobianco, Enrico, 2003. "Empirical volatility analysis: feature detection and signal extraction with function dictionaries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 319(C), pages 495-518.
    4. Vuorenmaa, Tommi A., 2005. "A wavelet analysis of scaling laws and long-memory in stock market volatility," Research Discussion Papers 27/2005, Bank of Finland.
    5. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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