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Exploiting Intraday Decompositions in Realized Volatility Forecasting: A Forecast Reconciliation Approach

Author

Listed:
  • Massimiliano Caporin
  • Tommaso Di Fonzo
  • Daniele Girolimetto
Abstract
We address the construction of Realized Variance (RV) forecasts by exploiting the hierarchical structure implicit in available decompositions of RV. We propose a post-forecasting approach that utilizes bottom-up and regression-based reconciliation methods. By using data referred to the Dow Jones Industrial Average Index and to its constituents we show that exploiting the informative content of hierarchies improves the forecast accuracy. Forecasting performance is evaluated out-of-sample based on the empirical MSE and QLIKE criteria as well as using the Model Confidence Set approach.

Suggested Citation

  • Massimiliano Caporin & Tommaso Di Fonzo & Daniele Girolimetto, 2024. "Exploiting Intraday Decompositions in Realized Volatility Forecasting: A Forecast Reconciliation Approach," Journal of Financial Econometrics, Oxford University Press, vol. 22(5), pages 1759-1784.
    • Handle: RePEc:oup:jfinec:v:22:y:2024:i:5:p:1759-1784.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbae014
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    More about this item

    Keywords

    forecast reconciliation; good and bad volatility; hierarchical forecasting; realized volatility;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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