Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2017
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.
Keywords
autoregressive conditional duration model, high-frequency data, integrated volatility, time-transformation function
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometrics
Volume
5
Issue
4
First Page
1
Last Page
19
ISSN
2225-1146
Identifier
10.3390/econometrics5040051
Publisher
MDPI
Citation
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.
Available at: https://ink.library.smu.edu.sg/soe_research/2130
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3390/econometrics5040051