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Methods of Volatility Estimation and Forecasting

In: Modelling and Forecasting High Frequency Financial Data

Author

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  • Stavros Degiannakis
  • Christos Floros

Abstract

This chapter reviews the most broadly used methods of volatility estimation and forecasting. Based on the daily log-returns, the ARCH, or Autoregressive Conditionally Heteroscedastic, process is a widely applied method in estimating and forecasting the unobserved asset’s volatility. Based on the intraday realized volatility, the ARFIMA, or Autoregressive Fractionally Integrated Moving Average, model is a broadly applied method for estimating and forecasting realized volatility. The programs on which the estimation and forecasting is based are constructed. Moreover, the most commonly used methods (evaluation or loss functions) for comparing the forecasting ability of the candidate models are presented.

Suggested Citation

  • Stavros Degiannakis & Christos Floros, 2015. "Methods of Volatility Estimation and Forecasting," Palgrave Macmillan Books, in: Modelling and Forecasting High Frequency Financial Data, chapter 3, pages 58-109, Palgrave Macmillan.
    • Handle: RePEc:pal:palchp:978-1-137-39649-5_3
    DOI: 10.1057/9781137396495_3
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    Cited by:

    1. Liu Ziyin & Kentaro Minami & Kentaro Imajo, 2021. "Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction," Papers 2106.04114, arXiv.org, revised Dec 2022.

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