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Volatility

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Applications in Finance, Investments, and Banking

Part of the book series: Advances in Computational Economics ((AICE,volume 9))

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Abstract

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.

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Authors and Affiliations

  1. Banque Paribas, France

    Ronald C. Heynen

  2. Bank of America, USA

    Harry M. Kat

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  1. Ronald C. Heynen
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  2. Harry M. Kat
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  1. CISDM, School of Management, University of Massachusetts, USA

    Thomas Schneeweis

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© 1999 Springer Science+Business Media New York

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Heynen, R.C., Kat, H.M. (1999). Volatility. In: Ho, D., Schneeweis, T. (eds) Applications in Finance, Investments, and Banking. Advances in Computational Economics, vol 9. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3007-4_6

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  • DOI: https://doi.org/10.1007/978-1-4757-3007-4_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-5062-8

  • Online ISBN: 978-1-4757-3007-4

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