Realized volatility forecast of financial futures using time-varying HAR latent factor models

We forecast realized volatilities by developing a time-varying heterogeneous autoregressive (HAR) latent factor model with dynamic model average (DMA) and dynamic model selection (DMS) approaches. The number of latent factors is determined using Chan and Grant's (2016) deviation information cri...

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Bibliographic Details
Published inJournal of management science and engineering (Online) Vol. 8; no. 2; pp. 214 - 243
Main Authors Luo, Jiawen, Chen, Zhenbiao, Wang, Shengquan
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.06.2023
KeAi Communications Co., Ltd
Subjects
Bayesian approaches
HAR latent factor models
Realized volatility forecast
Stock index
Time-varying
Treasury bond futures
Realized volatility forecast
Bayesian approaches
HAR latent factor models
Treasury bond futures
Time-varying
Stock index
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Summary:We forecast realized volatilities by developing a time-varying heterogeneous autoregressive (HAR) latent factor model with dynamic model average (DMA) and dynamic model selection (DMS) approaches. The number of latent factors is determined using Chan and Grant's (2016) deviation information criteria. The predictors in our model include lagged daily, weekly, and monthly volatility variables, the corresponding volatility factors, and a speculation variable. In addition, the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models, including size, inclusion probabilities, and coefficients, are examined. We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts. Furthermore, the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.
ISSN:2096-2320
2589-5532
DOI:10.1016/j.jmse.2022.10.005