Business time sampling scheme with applications to testing semi-martingale hypothesis and estimating integrated volatility

• Main Authors: DONG, Yingjie, TSE, Yiu Kuen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: autoregressive conditional duration model; high-frequency data; integrated volatility; time-transformation function; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2130; https://ink.library.smu.edu.sg/context/soe_research/article/3130/viewcontent/econometrics_05_00051.pdf
• Institution: Singapore Management University

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.