Overnight return in stock market is one kind of information that can reflect the volatility of the corresponding financial instrument. However, some volatility estimators, either based on range-based or high-frequency data, do not include this information in their formulations. In this study, we investigate the impact of overnight return on Engle’s Multiplicative Error Model (MEM). Garman’s and Hansen’s whole-day-based estimators are studied to demonstrate the effects under minimum-variance situations. Besides, a general framework for incorporating overnight information is proposed and the results are discussed. Our findings demonstrate that overnight return gives a non-monotonic influence and it does contain useful information for predicting the CBOE volatility indexes under specific combinations.
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