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

Journal of Risk, Forthcoming

19 Pages Posted: 25 Apr 2016 Last revised: 25 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: April 27, 2016

Abstract

No. Conditional autocorrelation in realized shocks due to misspecification in expected return process affects the relative performance of longer-horizon volatility predictions of models using different frequencies of data. This is because, for multi-step forecasts of volatility, small violations of residual serial independence are compounded in temporal aggregation. In this paper, we show that the 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 large cross section of stocks at any given point in time, and that this misspecification can substantially decrease the accuracy of multi-step forecasts generated by higher frequency models. Comparing the monthly volatility predictions from daily models and monthly models, we show that there is a trade-off between the gains from high-frequency data and the susceptibility of its multi-period ahead forecasts to returns model misspecification.

Keywords: Long-Horizon Volatility Forecast, Temporal Aggregation, Model Selection, Risk Management

JEL Classification: C22, C58, C53

Suggested Citation

Charoenwong, Ben and Feng, Guanhao, Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility? (April 27, 2016). Journal of Risk, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2769036 or http://dx.doi.org/10.2139/ssrn.2769036

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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