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Open AccessArticle

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

1
Department of Economics, Catholic University of Brasilia, 71966-700 Brasilia, Brazil
2
Department of Statistics, University of Brasilia, 70910-900 Brasilia, Brazil
3
Department of Economics, Federal University of Pelotas, 96010-610 Pelotas, Brazil
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(3), 45; https://doi.org/10.3390/jrfm13030045
Received: 24 December 2019 / Revised: 19 February 2020 / Accepted: 26 February 2020 / Published: 3 March 2020
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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. View Full-Text
Keywords: generalized Birnbaum–Saunders distributions; ACD models; Box-Cox transformation; high-frequency financial data; goodness-of-fit generalized Birnbaum–Saunders distributions; ACD models; Box-Cox transformation; high-frequency financial data; goodness-of-fit
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Cunha, D.R.; Vila, R.; Saulo, H.; Fernandez, R.N. A General Family of Autoregressive Conditional Duration Models Applied to High-Frequency Financial Data. J. Risk Financial Manag. 2020, 13, 45.

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