Intraday VAR : A Copula-Based Approach

The availability of high-frequency trading data and developments in computing technology make it possible to evaluate Intraday Value-at-Risk (IVaR), a useful risk management tool for investors and regulators. In this paper, we propose a new model to evaluate and predict the IVaR for stocks based on irregularly spaced intraday transaction data filtered by volume events. Our model is developed to obtain the joint distribution of volume durations and corresponding returns with a copula, and IVaR predictions are obtained via Monte Carlo simulations based on the estimated model. Backtesting results for 30 randomly selected Chinese stocks show that our model outperforms a well-established alternative model in generating more precise IVaR over 30- and 60-minute horizons. We also find that the mean of the IVaR results exhibits a W-shaped intraday curve