Modeling Financial Durations Using Estimating Functions

• Activity Number: 604 - Recent Advances in High-Frequency and High-Dimensional Time Series
• Type: Topic Contributed
• Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Nalini Ravishanker* and Yaohua Zhang and Jian Zou and Aerambamoorthy Thavaneswaran
• Companies: University of Connecticut and University of Connecticut and Worcester Polytechnic Institute and University of Manitoba
• Keywords: Godambe information ; High-frequency financial data ; Inter-event durations ; Log-ACD models ; Martingale estimating functions ; Online recursions

Abstract

Accurate modeling of patterns in inter-event durations is of considerable interest in high-frequency financial data analysis. The class of logarithmic autoregressive conditional duration (Log ACD) models provides a rich framework for analyzing durations, and recent research is focused on developing fast and accurate methods for fitting these models to long time series of durations under least restrictive assumptions. This article describes an optimal semi-parametric modeling approach using martingale estimating functions which only requires assumptions on the first few conditional moments of the durations. We introduce three approaches for parameter estimation, including solution of nonlinear estimating equations, recursive formulas for the vector-valued parameter estimates, and iterated component-wise scalar recursions. Effective starting values from an approximating time series model increase the accuracy of the final estimates. We demonstrate our approach via a simulation study and a real data illustration based on high-frequency transaction level data on several stocks.