Bayesian inference for the Birnbaum–Saunders autoregressive conditional duration model with application to high-frequency financial data

2021 ◽  
pp. 1-15
Author(s):  
Nascimento Fernando ◽  
Leao Jeremias ◽  
Helton Saulo
Keyword(s):  
Bayesian Inference ◽  
High Frequency ◽  
Financial Data ◽  
Duration Model ◽  
Autoregressive Conditional Duration ◽  
Autoregressive Conditional Duration Model

Time Varying Trade Intensities and the Deutsche Telekom IPO / Zeitvariable Handelsintensitaten und die Deutsche Telekom IPO

2000 ◽  
Vol 220 (6) ◽  
Author(s):  
Reinhard Hujer ◽  
Joachim Grammig ◽  
Stefan Kokot
Keyword(s):  
Initial Public Offering ◽  
Structural Breaks ◽  
Duration Model ◽  
Time Varying ◽  
Threshold Autoregressive ◽  
Public Offering ◽  
Autoregressive Conditional Duration ◽  
Autoregressive Conditional Duration Model

SummaryWe apply the Threshold Autoregressive Conditional Duration Model (TACD) as proposed by Zhang, Russell, and Tsay (1999) to model the after market trading duration process associated with the initial public offering of the Deutsche Telekom AG share in November of 1996. Special emphasis is devoted to the empirical specification of intra-day seasonality and to the detection of non-stationarity and structural breaks in the trading process.

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