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Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment

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  • Deo, Rohit
  • Hurvich, Clifford
  • Lu, Yi

Abstract

We study the modeling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of large datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalizing the volatility in high frequency data is proposed, that allows for slowly varying seasonality. Using both simulated as well as real data, we compare the forecasting performance of the LMSV model for forecasting realized volatility to that of a linear long memory model fit to the log realized volatility. The performance of the new seasonal adjustment is also compared to a recently proposed procedure using real data.
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Suggested Citation

  • Deo, Rohit & Hurvich, Clifford & Lu, Yi, 2006. "Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 29-58.
    • Handle: RePEc:eee:econom:v:131:y:2006:i:1-2:p:29-58
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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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