Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility?
|Journal / Publication||Journal of Risk|
|Online published||11 May 2017|
|Publication status||Published - Jun 2017|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85022222620&origin=recordpage|
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.
- Iterated forecasts, Long-horizon volatility, Mixed data frequency, Model selection, Risk management, Temporal aggregation
Journal of Risk, Vol. 19, No. 5, 06.2017, p. 55-75.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Charoenwong, B & Feng, G 2017, 'Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility?', Journal of Risk, vol. 19, no. 5, pp. 55-75. https://doi.org/10.21314/JOR.2017.360
Charoenwong, B., & Feng, G. (2017). Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility? Journal of Risk, 19(5), 55-75. https://doi.org/10.21314/JOR.2017.360
Charoenwong B, Feng G. Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility? Journal of Risk. 2017 Jun;19(5):55-75. https://doi.org/10.21314/JOR.2017.360