Published

2016-07-01

Conditional Duration Model and Unobserved Market Heterogeneity of Traders. An Infinite Mixture of Non–Exponentials

Modelo de duración condicionada y heterogeneidad inobservada de los agentes. Una mezcla infinita de distribuciones no exponenciales

Keywords:

Autoregressive conditional duration model, Exponential dis- tribution, Gamma distribution, Heterogeneity, Reciprocal inverse Gaussian distribution (en)
Modelo de duración autorregresivo condicional, Distribución exponencial, Distribución Gamma,, Heterogeneidad, Distribución recíproca inversa gaussiana (es)

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Authors

  • Emilio Gómez Déniz University of Las Palmas de Gran Canaria and TIDES Institute, Las Palmas de Gran Canaria, Spain
  • Jorge Perez-Rodriguez University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
This paper extends the conditional duration model proposed by De Luca and Zuccolotto (2003), proposing an infinite mixture of distributions based on non–exponentials which accounts for the unobserved market heterogeneity of traders. The model we propose takes into account the fact that reaction times follow a gamma distribution and that the intensity parameter follows the reciprocal of an inverse Gaussian distribution. This extension allows us to capture not only various density shapes of durations, but also non–monotonic shapes of hazard functions. The model also allows us to test the unobserved heterogeneity of traders. This mixture model is easy to fit and characterises the behaviour of the conditional durations reasonably well.

Este trabajo extiende el modelo de duración condicionada propuesto por
Luca & Zuccolotto (2003) introduciendo una mezcla infinita de distribuciones no exponenciales que permite incorporar la heterogeneidad inobservada en el mercado por los agentes. El modelo propuesto tiene en cuenta el hecho de que el tiempo de respuesta sigue una distribución gamma y que el parámetro que mide la intensidad sigue una distribución recíproca inversa Gaussiana. Esta modelización permite no sólo capturar distintas formas de la distribución de la duración sino que también captura funciones de azar no monótonas. El modelo propuesto es fácil de ajustar a datos de duración proporcionando resultados razonables y competitivos con otros modelos utilizados en la literatura.

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