Original scientific paper
https://doi.org/10.1080/1331677X.2022.2107554
Daily return distribution forecast incorporating intraday high frequency information in China’s stock market
Yanyun Yao
Qiang Huang
Shangzhen Cai
Abstract
The stock market forecast is an important and challenging issue. Its
distribution forecast of returns can provide information that is more
complete, compared to point forecast and interval forecast. As intraday
high-frequency information is available, we incorporate intraday
returns into the predictive modelling of daily return distribution in
two ways: realized volatility and scale calibration. Three parametric
models, EGARCH, EGARCH-X, and realGARCH, and two nonparametric
models, NP and realNP, are used. Our improved NP model, the
realNP model, is based on intraday returns calibration. The results
show that intraday information improves goodness-of-fit and forecasting
effect, and the realGARCH model is relatively the best.
According to the realNP model results, the intraday returns can only
contribute about a 30% description of the daily distribution and less
than 1% information for a one-step-ahead distribution forecast.
Furthermore, three combinations are considered, and the log-score
and CRPS combinations are found to have direction predictability
and excess profitability statistically. The non-short-selling situation
consistently has more excess profits than the short-selling situation,
which implies that the non-short-selling rule protects investors. This
study reveals the importance of incorporating intraday information
and model combinations for stock market forecast modelling.
Keywords
Intraday information; distribution forecast; realized volatility; scale calibration; model combination
Hrčak ID:
306641
URI
Publication date:
30.4.2023.
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