Publication Type

Master Thesis

Publication Date



The purpose of this study is to investigate stock volatility and forecasting performance of different volatility models over high-frequency intervals. The multiplicative component model that decomposes the conditional variance into a daily component and a periodicity component is studied with different specifications. This model is applied to 30 stocks. For the daily component, both parametric and non-parametric measures are considered. 12 models that capture the long memory feature of volatility are examined. Our results show the HAR-MEM model with overnight jump and the HAR-MEM model have the best forecasting performance among 12 models, and adding an overnight return term improves model’s forecasting ability. Periodicity component is captured by the proportion of summation of intraday volatility to summation of daily volatility over some time period. In comparison with the literature, our specification of periodicity component has slightly better forecasting performance in the first 2-hour volatility.


Intraday volatility, Multiplicative component model, Long memory property

Degree Awarded

MSc in Economics




Tse Xiu Kuen

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