Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195435 
Year of Publication: 
2017
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 5 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2017 [Pages:] 1-19
Publisher: 
MDPI, Basel
Abstract: 
We propose a new method to implement the Business Time Sampling (BTS) scheme for high-frequency financial data. We compute a time-transformation (TT) function using the intraday integrated volatility estimated by a jump-robust method. The BTS transactions are obtained using the inverse of the TT function. Using our sampled BTS transactions, we test the semi-martingale hypothesis of the stock log-price process and estimate the daily realized volatility. Our method improves the normality approximation of the standardized business-time return distribution. Our Monte Carlo results show that the integrated volatility estimates using our proposed sampling strategy provide smaller root mean-squared error.
Subjects: 
autoregressive conditional duration model
high-frequency data
integrated volatility
time-transformation function
JEL: 
C410
G120
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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