The stochastic conditional duration model: a latent factor model for the analysis of financial durations

By Luc Bauwens, David Veredas. 2004

102 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/265d82077e2f458825f0fab9cd26925c7b170506

Citations in the corpus

• A family of autoregressive conditional duration models (2006, unknown): “The seminal work of Engle and Russell (1998) hoisted a great interest in the implications of price and trade durations in empirical finance.”
• Stochastic Conditional Intensity Processes (2006, unknown): “Following the literature on latent factor models [Taylor (1982, 1986), Bauwens and Veredas (2004)], we assume * i to be conditionally i.i.d. lognormally distributed, 1 that is, ln * i jF * t iÀ1 $ i.i.d. Nðm * i ,1Þ: ð3Þ It is assumed that * i has left-continuous sample paths with right-hand limits.”
• Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models (2016, unknown): “We consider dynamic count, intensity, duration, volatility and copula densities and focus on three approaches for modelling the time-varying parameters of interest: nonlinear non-Gaussian state space models as representatives of parameter-driven specifications; the flexible observation-driven genera”
• Autoregressive Conditional Duration Models in Finance: A Survey of the Theoretical and Empirical Literature (2008, unknown): “Among the most relevant variables for investigating market microstructure effects, the lags of the following are typically used in analyses of stock markets (Engle and Russell, 1998; Bauwens and Giot, 2000; Bauwens and Giot, 2003; Bauwens and Veredas, 2004).”
• Maximization by Parts in Likelihood Inference (2005, unknown): “, Engle and Russell 1998; Bauwens and Veredas 2004) that the distribution of trading durations is heavy-tailed. Let {di, i = 1, . . . ,n} be a sequence of trading durations. A stochastic conditional duration model proposed by Bauwens and Veredas (2004) takes the form of a state-space model,”