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Econometrics 2017, 5(4), 51; doi:10.3390/econometrics5040051

Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility

1
Business School, University of International Business and Economics, 10 Huixin Dongjie, Beijing 100029, China
2
School of Economics, Singapore Management University, Singapore 178903, Singapore
*
Author to whom correspondence should be addressed.
Academic Editors: Deniz Erdemlioglu, Olivier Scaillet and Kamil Yilmaz
Received: 3 August 2017 / Revised: 28 September 2017 / Accepted: 17 October 2017 / Published: 13 November 2017
(This article belongs to the Special Issue Volatility Modeling)
View Full-Text   |   Download PDF [574 KB, uploaded 13 November 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. View Full-Text
Keywords: autoregressive conditional duration model; high-frequency data; integrated volatility; time-transformation function autoregressive conditional duration model; high-frequency data; integrated volatility; time-transformation function
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dong, Y.; Tse, Y.-K. Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility. Econometrics 2017, 5, 51.

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