Publication details

The effect of non-trading days on volatility forecasts in equity markets

Authors

LYÓCSA Štefan MOLNÁR Peter

Year of publication 2017
Type Article in Periodical
Magazine / Source Finance Research Letters
MU Faculty or unit

Faculty of Economics and Administration

Citation
Web https://www.sciencedirect.com/science/article/pii/S1544612317300181?via%3Dihub
Doi http://dx.doi.org/10.1016/j.frl.2017.07.002
Field Management and administrative
Keywords realized volatility; volatility forecasting; non-trading days
Description Weekends and holidays lead to gaps in daily financial data. Standard models ignore these irregularities. Because this issue is particularly important for persistent time series, we focus on volatility modelling, specifically modelling of realized volatility. We suggest a simple way of adjusting volatility models, which we illustrate on an AR(1) model and the HAR model of Corsi (2009). We investigate daily series of realized volatilities for 21 equity indices around the world, covering more than 15 years, and we find that our extension improves the volatility models—both in sample and out of sample. For HAR models and for consecutive trading days, the mean squared error decreased by 2.34% in average and for the QLIKE loss function by 1.41%.

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