Intraday value-at-risk.

In this paper, we apply a collection of parametric (Normal, Normal GARCH, Student GARCH, RiskMetrics and high-frequency duration models) and non-parametric (empirical quantile, extreme distributions models) Value-at-Risk (VaR) techniques to intraday data for three stocks traded on the NewY ork Stock Exchange. Because of the small time horizon of the intraday returns (15 and 30 minute returns), intraday VaR can be useful to market participants (traders, market makers)involved in frequent trading. As expected, the volatility features an important intraday seasonality, which must be removed prior to using theVaR models. The estimation and assessment of the VaR techniques indicate that the data displays a high kurtosis (fat tails), and that VaR models should take this important feature into account. More particularly, Student GARCH, empirical quantile and extreme distributions models perform relatively well.Intraday volatility, Intraday Value-at-Risk, Duration models, NYSE.