Panel intensity models with latent factors: An application to the trading dynamics on the foreign exchange market

We develop a panel intensity model, with a time varying latent factor, which captures the influence of unobserved time effects and allows for correlation across individuals. The model is designed to analyze individual trading behavior on the basis of trading activity datasets, which are characterized by four dimensions: an irregularly-spaced time scale, trading activity types, trading instruments and investors. Our approach extends the stochastic conditional intensity model of Bauwens & Hautsch (2006) to panel duration data. We show how to estimate the model parameters by a simulated maximum likelihood technique adopting the efficient importance sampling approach of Richard & Zhang (2005). We provide an application to a trading activity dataset from an internet trading platform in the foreign exchange market and we find support for the presence of behavioral biases and discuss implications for portfolio theory.