Publication Type
Journal Article
Version
submittedVersion
Publication Date
12-2015
Abstract
We propose to compute the Intraday Value-at-Risk (IVaR) for stocks using real-time transaction data. Tick-by-tick data filtered by price duration are modeled using a two-state asymmetric autoregressive conditional duration (AACD) model, and the IVaR is calculated using Monte Carlo simulation based on the estimated AACD model. Backtesting results for the New York Stock Exchange (NYSE) show that the IVaR calculated using the AACD method outperforms those using the Dionne et al. (2009) and Giot (2005) methods.
Keywords
High-frequency transaction data, Market microstructure noise, Asymmetric autoregressive conditional duration model, Intraday Value-at-Risk, Backtesting
Discipline
Econometrics | Portfolio and Security Analysis
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
189
Issue
2
First Page
437
Last Page
446
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2015.03.035
Publisher
Elsevier
Citation
LIU, Shouwei and TSE, Yiu Kuen.
Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach. (2015). Journal of Econometrics. 189, (2), 437-446.
Available at: https://ink.library.smu.edu.sg/soe_research/1871
Copyright Owner and License
Authors
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/10.1016/j.jeconom.2015.03.035