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Full-Text Articles in Econometrics
Estimation Of Monthly Volatility: An Empirical Comparison Of Realized Volatility, Garch And Acd-Icv Methods, Shouwei Liu, Yiu Kuen Tse
Estimation Of Monthly Volatility: An Empirical Comparison Of Realized Volatility, Garch And Acd-Icv Methods, Shouwei Liu, Yiu Kuen Tse
Research Collection School Of Economics
We apply the ACD-ICV method proposed by Tse and Yang (2011) for the estimation of intraday volatility to estimate monthly volatility, and empirically compare this method against the realized volatility (RV) and generalized autoregressive conditional heteroskedasticity (GARCH) methods. Our Monte Carlo results show that the ACD-ICV method performs well against the other two methods. Evidence on the Chicago Board Options Exchange volatility index (VIX) shows that it predicts the ACD-ICV volatility estimates better than it predicts the RV estimates. While the RV method is popular for the estimation of monthly volatility, its performance is inferior to the GARCH method.
Using High-Frequency Transaction Data To Estimate The Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Using High-Frequency Transaction Data To Estimate The Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Research Collection School Of Economics
This paper applies the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) to estimate the probability of informed trading (PIN) using irregularly spaced transaction data. We model trade direction (buy versus sell orders) and the duration between trades jointly. Unlike the Easley, Hvidkjaer, and O'Hara (2002) approach, which uses the aggregate numbers of daily buy and sell orders to estimate PIN, our methodology allows for interactions between consecutive buy-sell orders and accounts for the duration between trades and the volume of trade. We extend the Easley–Hvidkjaer–O'Hara framework by allowing the probabilities of good news and bad news …