Irregularly Spaced Intraday Value at Risk (ISIVaR) Models : Forecasting and Predictive Abilities

Christophe Hurlin (1) , Gilbert Colletaz (1) , Sessi Tokpavi (1)

1 LEO - Laboratoire d'économie d'Orleans [2008-2011]

The objective of this paper is to propose a market risk measure defined in price event time and a suitable backtesting procedure for irregularly spaced data. Firstly, we combine Autoregressive Conditional Duration models for price movements and a non parametric quantile estimation to derive a semi-parametric Irregularly Spaced Intraday Value at Risk (ISIVaR) model. This ISIVaR measure gives two information: the expected duration for the next price event and the related VaR. Secondly, we use a GMM approach to develop a backtest and investigate its finite sample properties through numerical Monte Carlo simulations. Finally, we propose an application to two NYSE stocks.

Mots clés: Value at Risk; High-frequency data; ACD models; Irregularly spaced market risk models; Backtesting

Domaines: Economies et finances

Christophe Hurlin, Gilbert Colletaz, Sessi Tokpavi. Irregularly Spaced Intraday Value at Risk (ISIVaR) Models : Forecasting and Predictive Abilities. 2007. ⟨halshs-00162440⟩