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

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

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...

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Bibliographic Details
Main Authors: Danúbia R. Cunha, Roberto Vila, Helton Saulo, Rodrigo N. Fernandez
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Journal of Risk and Financial Management
Subjects:
generalized birnbaum–saunders distributions
acd models
box-cox transformation
high-frequency financial data
goodness-of-fit
Online Access:https://www.mdpi.com/1911-8074/13/3/45
Description
Summary:In this paper, we propose a general family of Birnbaum&#8722;Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum&#8722;Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter <inline-formula> <math display="inline"> <semantics> <mi>&#955;</mi> </semantics> </math> </inline-formula> 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.
ISSN:1911-8074

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