Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility
Yingjie Dong () and
Yiu-Kuen Tse ()
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Yingjie Dong: Business School, University of International Business and Economics, 10 Huixin Dongjie, Beijing 100029, China
Yiu-Kuen Tse: School of Economics, Singapore Management University, Singapore 178903, Singapore
Econometrics, 2017, vol. 5, issue 4, 1-19
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.
Keywords: autoregressive conditional duration model; high-frequency data; integrated volatility; time-transformation function (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:5:y:2017:i:4:p:51-:d:118613
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