Abstract
This paper investigates the use of tick-by-tick data for intraday market risk measurement. We propose a method to compute an Intraday Value at Risk based on irregularly spaced high-frequency data and an intraday Monte Carlo simulation. A log-ACD-ARMA-EGARCH model is used to specify the joint density of the marked point process of durations and high-frequency returns. We apply our methodology to transaction data for three stocks actively traded on the Toronto Stock Exchange. Compared to traditional techniques applied to intraday data, our methodology has two main advantages. First, our risk measure has a higher informational content as it takes into account all observations. On the total risk measure, our method allows for distinguishing the effect of random trade durations from the effect of random returns, and for analyzing the interaction between these factors. Thus, we find that the information contained in the time between transactions is relevant to risk analysis, which is consistent with predictions from asymmetric-information models in the market microstructure literature. Second, once the model has been estimated, the IVaR can be computed by any trader for any time horizon based on the same information and with no need of sampling the data and estimating the model again when the horizon changes. Backtesting results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active in the market.
| Original language | English |
|---|---|
| Pages (from-to) | 777-792 |
| Number of pages | 16 |
| Journal | Journal of Empirical Finance |
| Volume | 16 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - Dec 2009 |
Bibliographical note
Funding Information:We are very grateful to Christian Gouriéroux and Denitsa Stefanova for valuable remarks and suggestions. The authors thank the two anonymous referees for constructive criticism. We have also benefited from remarks and comments through correspondence from Patrick Burns, Peter Christoffersen, Pierre Giot, Joachim Grammig, Erick Rengifo, and David Veredas. We also thank Iraj Fooladi, Geneviève Gauthier, Alain Guay, Khemais Hammami, Cristian Popescu, Jean-Guy Simonato, Joshua Slive, and participants at the 2006 Financial Management Association Annual Meeting, the 2006 Northern Finance Association Conference, the 2006 Econometric Society European Meeting, the 2006 European Financial Management Association Conference, the Bank of France Microstructure of Financial and Money Markets Conference, the 2005 International Conference on New Financial Market Structures, and seminar participants at Bank of Canada, Concordia University, Dalhousie University, HEC Montréal, in particular, Greg Bauer, Umberto Cherubini, Chris D'Souza, David Goldreich, Ingrid Lo, Albert Menkveld, Ludovic Mercier, Stephen Sapp, Kashi Nath Tiwari for helpful comments on a previous version of the paper. Jean-David Fournier, Francois Guertin, and Mohamed Jabir provided useful research assistance. Maria Pacurar acknowledges financial support from the Institut de Finance Mathématique de Montréal (IFM2), the Centre for Research on E-Finance (CREF), the Canada Research Chair in Risk Management and the School of Business Administration, Dalhousie University.
ASJC Scopus Subject Areas
- Finance
- Economics and Econometrics