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.
Highlights
In high-frequency financial data analysis, researchers usually do not use all available data but would select a subgrid of transactions
Using the sequential jump-detection procedure of Andersen et al (2010), we investigate the proportion of detected jumps when different sampling intervals and sampling schemes are used
We compute Tripower Realized Volatility (TRV) based on the Calendar Time Sampling (CTS), Tick Time Sampling (TTS) and Business Time Sampling (BTS) returns, with and without subsampling
Summary
In high-frequency financial data analysis, researchers usually do not use all available data but would select a subgrid of transactions. Due to the leverage effect or varying volatility, the calendar-time returns may not be iid normal even if the price process is a continuous local martingale. One drawback of the method in Andersen et al (2007, 2010) is that its performance in obtaining returns with approximately equal volatility over each interval deteriorates as the sampling frequency increases. We propose a new method to implement the BTS scheme, which has better performance as the sampling frequency increases and needs no threshold. The value of the TT function corresponds to the cumulative increments of the estimated intraday integrated volatility over time and the sampled BTS transactions are obtained using the inverse of the TT function. We consider the Realized Volatility (RV) estimates for daily integrated volatility when the returns are sampled using the BTS, CTS and TTS schemes. Some additional results can be found in the accompanying online supplementary material
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