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.
autoregressive conditional duration model, high-frequency data, integrated volatility, time-transformation function
DONG, Yingjie and TSE, Yiu Kuen.
Business time sampling scheme with applications to testing semi-martingale hypothesis and estimating integrated volatility. (2017). Econometrics. 5, (4), 1-19. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/2130
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