The Lognormal Autoregressive Conditional Duration (LNACD) Model and a Comparison with an Alternative ACD Models

2013 ◽  
Author(s):  
Yongdeng Xu
Keyword(s):  
Acd Models ◽  
Autoregressive Conditional Duration

scholarly journals A General Family of Autoregressive Conditional Duration Models Applied to High-Frequency Financial Data

2020 ◽  
Vol 13 (3) ◽  
pp. 45
Author(s):  
Danúbia R. Cunha ◽  
Roberto Vila ◽  
Helton Saulo ◽  
Rodrigo N. Fernandez
Keyword(s):  
New York ◽  
Stock Exchange ◽  
New York Stock Exchange ◽  
Monte Carlo Simulation Study ◽  
General Family ◽  
Conditional Median ◽  
York Stock Exchange ◽  
Acd Models ◽  
Box Cox Transformation ◽  
Autoregressive Conditional Duration

In this paper, we propose a general family of Birnbaum–Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum–Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter λ to the conditional median dynamics and an asymmetric response to shocks; this is denoted by GBS-AACD. We then carry out a Monte Carlo simulation study to evaluate the performance of the GBS-ACD models. Finally, an illustration of the proposed models is made by using New York stock exchange (NYSE) transaction data.

Nonstationary autoregressive conditional duration models

2017 ◽  
Vol 21 (4) ◽  
Author(s):  
Anuj Mishra ◽  
Thekke Variyam Ramanathan
Keyword(s):  
Duration Models ◽  
Transaction Data ◽  
New Model ◽  
Acd Models ◽  
Autoregressive Conditional Duration

AbstractRecently, there has been a growing interest in studying the autoregressive conditional duration (ACD) models, originally introduced by (Engle, R. F., and J. R. Russell. 1998. “Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data.

scholarly journals ERGODICITY, MIXING, AND EXISTENCE OF MOMENTS OF A CLASS OF MARKOV MODELS WITH APPLICATIONS TO GARCH AND ACD MODELS

2008 ◽  
Vol 24 (5) ◽  
pp. 1291-1320 ◽  
Author(s):  
Mika Meitz ◽  
Pentti Saikkonen
Keyword(s):  
Markov Models ◽  
Sufficient Conditions ◽  
Geometric Ergodicity ◽  
First Order ◽  
Initial Values ◽  
Special Cases ◽  
Acd Models ◽  
Necessary And Sufficient ◽  
Autoregressive Conditional Duration ◽  
Unobservable Component

This paper studies a class of Markov models that consist of two components. Typically, one of the components is observable and the other is unobservable or “hidden.” Conditions under which geometric ergodicity of the unobservable component is inherited by the joint process formed of the two components are given. This implies existence of initial values such that the joint process is strictly stationary and β-mixing. In addition to this, conditions for the existence of moments are also obtained, and extensions to the case of nonstationary initial values are provided. All these results are applied to a general model that includes as special cases various first-order generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional duration (ACD) models with possibly complicated nonlinear structures. The results only require mild moment assumptions and in some cases provide necessary and sufficient conditions for geometric ergodicity.

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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