Out‐of‐sample volatility prediction: A new mixed‐frequency approach

Yaojie Zhang, Feng Ma, Tianyi Wang () and Li Liu

Journal of Forecasting, 2019, vol. 38, issue 7, 669-680

Abstract: This paper proposes a new mixed‐frequency approach to predict stock return volatilities out‐of‐sample. Based on the strategy of momentum of predictability (MoP), our mixed‐frequency approach has a model switching mechanism that switches between generalized autoregressive conditional heteroskedasticity (GARCH)‐class models that only use low‐frequency data and heterogeneous autoregressive models of realized volatility (HAR‐RV)‐type that only use high‐frequency data. The MoP model simply selects a forecast with relatively good past performance between the GARCH‐class and HAR‐RV‐type forecasts. The model confidence set (MCS) test shows that our MoP strategy significantly outperforms the competing models, which is robust to various settings. The MoP test shows that a relatively good recent past forecasting performance of the GARCH‐class or HAR‐RV‐type model is significantly associated with a relatively good current performance, supporting the success of the MoP model.

Date: 2019
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:38:y:2019:i:7:p:669-680

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