Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

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.