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Activity Number:
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92
- Time Series and Finance
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Type:
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Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #317749
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Title:
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On a Quantile Autoregressive Conditional Duration Model Applied to High-Frequency Financial Data
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Author(s):
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Helton Saulo* and Narayanaswamy Balakrishnan and Roberto Vila
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Companies:
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University of Brasilia and McMaster University and University of Brasilia
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Keywords:
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Skewed Birnbaum-Saunders distribution;
Conditional quantile;
ECM algorithm;
Monte Carlo simulation;
Financial transaction data
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Abstract:
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Autoregressive conditional duration (ACD) models are primarily used to deal with data arising from times between two successive events. These models are usually specified in terms of a time-varying conditional mean or median duration. In this paper, we relax this assumption and consider a conditional quantile approach to facilitate the modeling of different percentiles. The proposed ACD quantile model is based on a skewed version of Birnbaum-Saunders distribution, which provides better fitting of the tails than the traditional Birnbaum-Saunders distribution, in addition to advancing the implementation of an expectation conditional maximization (ECM) algorithm. A Monte Carlo simulation study is performed to assess the behavior of the model as well as the parameter estimation method and to evaluate a form of residual. A real financial transaction data set is finally analyzed to illustrate the proposed approach.
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Authors who are presenting talks have a * after their name.
