2023/06/23 by Helton Saulo, Saulo, Helton, Suvra Pal +3
Decision Sciences · Economics, Econometrics and Finance · #62F99 #62M99 #Advanced Statistical Process Monitoring #Applications (stat.AP) #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #G.0 #G.3
paper · pdf · doi:10.48550/arxiv.2306.13764
openalex publication_date 2023/06/23 · openalex created_date 2023/06/28 · openalex updated_date 2026/07/28
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.