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 ab-solute 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 volatil-ity. 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. Content
This study investigates the practical importance of several VaR modeling and forecasting issues in t...
This paper investigates the information content of the ex post overnight return for one-day-ahead eq...
This paper investigates the information content of the ex post overnight return for one-day-ahead eq...
By decomposing close to close returns into close to open returns (overnight returns) and open to clo...
This paper provides a comprehensive evaluation of the predictive ability of information accumulated ...
This paper provides a comprehensive evaluation of the predictive ability of information accumulated ...
This paper examines the benefits to forecasters of decomposing close-to-close return volatility into...
This paper examines the benefits to forecasters of decomposing close-to-close return volatility into...
This paper examines the benefits to forecasters of decomposing close-to-close return volatility into...
This paper examines the benefits to forecasters of decomposing close-to-close return volatility into...
We use realized volatilities based on after-hours high frequency stock returns to predict next day s...
We investigate the two components of the total daily return (close-to-close), the overnight return (...
We investigate price discovery over the 24-hour trading day for equities, currencies, bonds, and com...
Previous research has identified overnight public information as the cause of higher opening returns...
We present a novel risk measurement model capable of capturing overnight risk i.e. the risk encounte...
This study investigates the practical importance of several VaR modeling and forecasting issues in t...
This paper investigates the information content of the ex post overnight return for one-day-ahead eq...
This paper investigates the information content of the ex post overnight return for one-day-ahead eq...
By decomposing close to close returns into close to open returns (overnight returns) and open to clo...
This paper provides a comprehensive evaluation of the predictive ability of information accumulated ...
This paper provides a comprehensive evaluation of the predictive ability of information accumulated ...
This paper examines the benefits to forecasters of decomposing close-to-close return volatility into...
This paper examines the benefits to forecasters of decomposing close-to-close return volatility into...
This paper examines the benefits to forecasters of decomposing close-to-close return volatility into...
This paper examines the benefits to forecasters of decomposing close-to-close return volatility into...
We use realized volatilities based on after-hours high frequency stock returns to predict next day s...
We investigate the two components of the total daily return (close-to-close), the overnight return (...
We investigate price discovery over the 24-hour trading day for equities, currencies, bonds, and com...
Previous research has identified overnight public information as the cause of higher opening returns...
We present a novel risk measurement model capable of capturing overnight risk i.e. the risk encounte...
This study investigates the practical importance of several VaR modeling and forecasting issues in t...
This paper investigates the information content of the ex post overnight return for one-day-ahead eq...
This paper investigates the information content of the ex post overnight return for one-day-ahead eq...