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Information Content of Volatility Forecasts at Medium-term Horizons

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Listed:
  • John Galbraith
  • Turgut Kisinbay

Abstract

Using realized volatility to estimate daily conditional volatility of financial returns, we compare forecasts of daily volatility from standard QML-estimated GARCH models, and from projections on past realized volatilities obtained from high-frequency data. We consider horizons extending to thirty trading days. The forecasts are compared with the unconditional sample variance of daily returns treated as a daily volatility forecast, allowing us to estimate the maximum horizon at which the model-based forecasts provide forecasting power, measured by MSE reduction. Using data from a Toronto Stock Exchange equity index and foreign exchange returns (DM/$US and Yen/$US), we find evidence of forecasting power at horizons of up to thirty trading days, on each of the three financial returns series. We also find some evidence that the result of (e.g.) Bollerslev and Wright (2001), that projections on past realized volatility provide better 1-step forecasts than the QML-GARCH forecasts, appears to extend to longer horizons up to around ten to fifteen trading days. At longer horizons, there appears to be little to distinguish the forecast methods. En utilisant la volatilité réalisée pour estimer la volatilité conditionnelle quotidienne des rendements financiers, nous comparons les prévisions de volatilité quotidienne effectuées à partir de modèles GARCH-QVM standard et à partir de projections directes sur les volatilités réalisées. Nous considérons un horizon maximal de trente jours de transaction. Les prévisions sont comparées à la variance non conditionnelle des rendements quotidiens, ce qui nous permet d'estimer l'horizon maximal pour lequel les modèles détiennent un pouvoir de prévision. Nous utilisons des données de l'indice TSE 35 et des taux de change DM/US$ et Yen/US$, et nos résultats montrent qu'il y a un pouvoir de prédiction jusqu'à un horizon de trente jours, et ce, pour chacune des trois séries. Nous montrons aussi que le résultat de Bollerslev et Wright (2001), résultat indiquant que les projections sont supérieures sur l'horizon d'un jour, reste valide dans un horizon s'étendant jusqu'à dix ou quinze jours. Pour des horizons plus longs, les deux types de méthodes de prévision ne se différencient guère.

Suggested Citation

  • John Galbraith & Turgut Kisinbay, 2002. "Information Content of Volatility Forecasts at Medium-term Horizons," CIRANO Working Papers 2002s-21, CIRANO.
  • Handle: RePEc:cir:cirwor:2002s-21
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    File URL: http://www.cirano.qc.ca/files/publications/2002s-21.pdf
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    References listed on IDEAS

    as
    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    2. Nathalie de Marcellis-Warin & Erwann Michel-Kerjan, 2001. "The Public-Private Sector Risk-Sharing in the French Insurance "Cat. Nat. System"""," CIRANO Working Papers 2001s-60, CIRANO.
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    Cited by:

    1. repec:lan:wpaper:592830 is not listed on IDEAS
    2. Turgut Kısınbay, 2010. "Predictive ability of asymmetric volatility models at medium-term horizons," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3813-3829.

    More about this item

    Keywords

    GARCH; high-frequency data; integrated volatility; realized volatility; GARCH; données à haute fréquence; volatilité intégrée; volatilité réalisée;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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