Autoregressive Conditional Duration (ACD) Models in Finance: A Survey of the Theoretical and Empirical Literature

2006 ◽  
Author(s):  
Maria Pacurar
Keyword(s):  
Empirical Literature ◽  
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.

The Impact of Quantitative Easing on Emerging Markets – Literature Review

e-Finanse ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 67-76
Author(s):  
Piotr Bartkiewicz
Keyword(s):  
Emerging Markets ◽  
Large Scale ◽  
Empirical Literature ◽  
Quantitative Easing ◽  
Methodological Choices ◽  
The Subject ◽  
Methodological Approaches ◽  
The Impact ◽  
Sufficient Precision

AbstractThe article presents the results of the review of the empirical literature regarding the impact of quantitative easing (QE) on emerging markets (EMs). The subject is of interest to policymakers and researchers due to the increasingly larger role of EMs in the world economy and the large-scale capital flows occurring after 2009. The review is conducted in a systematic manner and takes into consideration different methodological choices, samples and measurement issues. The paper puts the summarized results in the context of transmission channels identified in the literature. There are few distinct methodological approaches present in the literature. While there is a consensus regarding the direction of the impact of QE on EMs, its size and durability have not yet been assessed with sufficient precision. In addition, there are clear gaps in the empirical findings, not least related to relative underrepresentation of the CEE region (in particular, Poland).

Improving the questions we ask, with Psychology and Cognitive Conflict.

2018 ◽  
Author(s):  
Brett Buttliere
Keyword(s):  
Problem Solving ◽  
Scientific Method ◽  
Meaning Making ◽  
Cognitive Conflict ◽  
Empirical Literature ◽  
Present Evidence ◽  
Scientific Papers ◽  
Science In Society

Over the last decade, there have been many suggestions to improve how scientists answer their questions, but far fewer attempt to improve the questions scientists are asking in the first place. The goal of the paper is then to examine and summarize synthesize the evidence on how to ask the best questions possible. First is a brief review of the philosophical and empirical literature on how the best science is done, which implicitly but not explicitly mentions the role of psychology and especially cognitive conflict. Then we more closely focus on the psychology of the scientist, finding that they are humans, engaged in a meaning making process, and that cognitive conflict is a necessary input for any learning or change in the system. The scientific method is, of course, a specialized meaning making process. We present evidence for this central role of cognitive conflict in science by examining the most discussed scientific papers between 2013 and 2017, which are, in general, controversial and about big problems (e.g., whether vaccines cause autism, how often doctors kill us with their mistakes). Toward the end we discuss the role of science in society, suggesting science itself is an uncertainty reducing and problem solving enterprise. From this basis we encourage scientists to take riskier stances on bigger topics, for the good of themselves and society generally.

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