Bivariate autoregressive conditional models: A new method for jointly modeling duration and number of transactions of irregularly spaced financial data

TL;DR

This paper introduces a novel bivariate autoregressive conditional duration model using log-symmetric distributions to jointly analyze transaction durations and counts in high-frequency financial data.

Contribution

It proposes a new bivariate ACD model based on log-symmetric distributions, enabling joint modeling of durations and transaction counts in irregularly spaced financial data.

Findings

Model effectively captures asymmetric and heavy-tailed data

Simulation confirms accurate estimation and residual evaluation

Real data analysis demonstrates practical applicability

Abstract

In this paper, a new approach to bivariate modeling of autoregressive conditional duration (ACD) models is proposed. Specifically, we consider the joint modeling of durations and the number of transactions made during the spell. The proposed bivariate ACD model is based on log-symmetric distributions, which are useful for modeling strictly positive, asymmetric and light- and heavy-tailed data, such as transaction-level high-frequency financial data. A Monte Carlo simulation is performed for the assessment of the estimation method and the evaluation of a form of residuals. A real financial transactions data set is analyzed in order to illustrate the proposed method.