Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility?

Posted: 13 May 2017

See all articles by Ben Charoenwong

Ben Charoenwong

National University of Singapore - Department of Finance

Guanhao Feng

City University of Hong Kong (CityUHK)

Date Written: May 11, 2017

Abstract

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.

Keywords: long-horizon volatility, iterated forecasts, temporal aggregation, model selection, risk management, mixed data frequency

Suggested Citation

Charoenwong, Ben and Feng, Guanhao, Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility? (May 11, 2017). Journal of Risk, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2966740

Ben Charoenwong (Contact Author)

National University of Singapore - Department of Finance ( email )

Mochtar Riady Building
15 Kent Ridge Drive
Singapore, 119245
Singapore

HOME PAGE: http://bizfaculty.nus.edu/faculty-profiles/519-ben

Guanhao Feng

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

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