Estimation of Monthly Volatility: An Empirical Comparison of Realized Volatility, GARCH and ACD-ICV Methods

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 Carl...

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Bibliographic Details
Main Authors: LIU, Shouwei, TSE, Yiu Kuen
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/soe_research/1476
https://ink.library.smu.edu.sg/context/soe_research/article/2475/viewcontent/2011___02___estimation_of_monthly_volatility.pdf
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Institution: Singapore Management University
Language: English
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Summary: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.