Publication Type

Journal Article

Publication Date

9-2017

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

Publication

Econometrics

Volume

5

Issue

4

First Page

1

Last Page

19

ISSN

2225-1146

Identifier

10.3390/econometrics5040051

Publisher

MDPI

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.3390/econometrics5040051

Included in

Econometrics Commons

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