Journal of Risk

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Does higher-frequency data always help to predict longer-horizon volatility?

Ben Charoenwong and Guanhao Feng

  • Direct forecasting models are precisely estimated but more sensitive to misspecification.
  • Violations of serial independence affect iterated forecast more than direct forecast.
  • Temporally aggregated longer horizon models are more robust to mean misspecification.
  • The conditional autocorrelation in realized shocks is useful to see this trade-off.

When it comes to forecasting long-horizon volatility, multistep-ahead iterated forecasts using higher-frequency data can be more efficient than one-step-ahead direct forecasts using lower-frequency data. However, small violations of model specification in either the volatility or expected return models are compounded in the forward iteration and temporal aggregation for the higher-frequency model. In this paper, we show that realized conditional autocorrelation in return residuals is a strong predictor of the relative performance of different frequency models of volatility. When the conditional autocorrelation is high, the higher-frequency model performs markedly worse than its lower-frequency counterpart. Empirically, we show that residual autocorrelation exists in the broad cross-section of stocks at any given point in time, and that this misspecification can substantially decrease the prediction performance of higher-frequency models. Comparing the monthly volatility predictions using daily and monthly data, we show a trade-off between the gains from higher-frequency data and the susceptibility of its multistep-ahead iterated forecasts to model misspecification.

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