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Forecasting Realized Volatility by Decomposition

Author

Listed:
  • Markku Lanne
Abstract
Forecasts of the realized volatility of the exchange rate returns of the Euro against the U.S. Dollar obtained directly and through decomposition are compared. Decomposing the realized volatility into its continuous sample path and jump components and modeling and forecasting them separately instead of directly forecasting the realized volatility is shown to lead to improved out-of-sample forecasts. Moreover, gains in forecast accuracy are robust with respect to the details of the decomposition.

Suggested Citation

  • Markku Lanne, 2006. "Forecasting Realized Volatility by Decomposition," Economics Working Papers ECO2006/20, European University Institute.
    • Handle: RePEc:eui:euiwps:eco2006/20
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    References listed on IDEAS

    as
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    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    3. Danielsson, J. & Payne, R., 2002. "Real trading patterns and prices in spot foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 21(2), pages 203-222, April.
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    Citations

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    Cited by:

    1. Basel M. A. Awartani, 2008. "Forecasting volatility with noisy jumps: an application to the Dow Jones Industrial Average stocks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 267-278.
    2. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    3. Milan Ficura & Jiri Witzany, 2016. "Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(4), pages 278-301, August.
    4. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.

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    More about this item

    Keywords

    Mixture model; Jump; Realized volatility; Gamma distribution;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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