• Ronald C. Heynen
  • Harry M. Kat
Part of the Advances in Computational Economics book series (AICE, volume 9)


In this chapter we provide an overview of the many volatility predictors that were suggested over time. We first discuss volatility predictors based on historical price and return data. Afterwards, we turn to predictors based on other sources of information. We concentrate on stock return volatility. The methods discussed are, however, equally valid for other assets. Although all predictors are discussed in isolation, it is important that a combination of individual forecasts may sometimes outperform any of its constituents. “The reason that a combined forecast may be preferable is that neither constituent forecast is using all of the data in the available information set in an optimum fashion”.1 In the discussion the terms “estimation”, “forecasting” and “prediction” are used interchangeably to emphasize the fact that we see volatility not just as a parameter of some probabilistic model which has to be estimated, but much more as a real-world variable the value of which must be predicted or forecasted.


Stock Return Option Price Conditional Variance Stochastic Volatility Call Option 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Ronald C. Heynen
    • 1
  • Harry M. Kat
    • 2
  1. 1.Banque ParibasFrance
  2. 2.Bank of AmericaUSA

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