Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach

Shouwei Liu and Yiu-Kuen Tse

Journal of Econometrics, 2015, vol. 189, issue 2, 437-446

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 (search for similar items in EconPapers)
JEL-codes: C41 G12 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:189:y:2015:i:2:p:437-446

DOI: 10.1016/j.jeconom.2015.03.035

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