The Predictability of Overnight Information
By decomposing close to close returns into close to open returns (overnight returns) and open to close returns (daytime returns), we test the predictability of overnight information, which is captured by absolute values of close to open returns, on daytime return volatility. Applying the stochastic...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2007
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| Online Access: | https://ink.library.smu.edu.sg/etd_coll/46 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1045&context=etd_coll |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | By decomposing close to close returns into close to open returns (overnight returns) and open to close returns (daytime returns), we test the predictability of overnight information, which is captured by absolute values of close to open returns, on daytime return volatility. Applying the stochastic volatility model, we find that overnight price changes contain important information to predict daytime volatility. The predictive power is highest at market opening and declines gradually over the trading day. Moreover, the predictive power is higher for inactive traded stocks than for actively traded stocks. |
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