Methods of Volatility Estimation and Forecasting

Stavros Degiannakis and Christos Floros

Chapter 3 in Modelling and Forecasting High Frequency Financial Data, 2015, pp 58-109 from Palgrave Macmillan

Abstract: 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.

Keywords: Loss Function; Option Price; Conditional Variance; Stochastic Volatility; Implied Volatility (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:pal:palchp:978-1-137-39649-5_3

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DOI: 10.1057/9781137396495_3

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