Using density forecasts, we compare the predictive performance of dur ation models that have been developed fo modelling intra-day data on stock markets. Our model portfolio encompasses the auto regressive conditional duration (ACD) model, its logarithmic version (Log-ACD), the threshold...

This paper proposes a class of asymmetric Autoregressive Conditional Duration models, which extends the ACD model of Engle and Russell (1997). The asymmetry consists of letting the duration process depend on the state of the price process in the beginning and at the end of the each duration. If...

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 New York...

In this paper we model Value-at-Risk (VaR) for daily stock index returns using a collection of parametric models of the ARCH family based on the skewed Student distribution. We show that models that rely on a symmetric density distribution for the error term underperform with respect to skewed...

This paper tests market co-integration, market leadership and price margins in the context of the recent development of European markets for imported off-season fresh fruit countries in the southern hemisphere. The Engle-Garner and Johansen co-integration tests show that the main European...

In this paper, we focus on the trade and quote data for the IBM stock traded at the NYSE. We present two different framworks for analyzing this dataset. First, using regularly sampled observations, we characterize the intraday volatility of the mid-point of the bid-ask quotes by estimating GARCH...