STATISTICS COLLOQUIUM
Nalini Ravishanker
Professor, Dept. of Statistics
University of Connecticut
Modeling Financial Durations using Penalized Estimating Functions
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 talk describes a semi-parametric modeling approach using Godambe-Durbin martingale estimating functions. This approach has wide applicability to several classes of linear and nonlinear time series. It only requires assumptions on the first few conditional moments of the process and does not require specification of its probability distribution. We discuss three approaches for parameter estimation: 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 illustrate our approach via a simulation study and a real data example based on high-frequency transaction level data on several stocks. We may use this approach for structural break detection in a retrospective and an online way.
DATE: Wednesday, March 7, 2018
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 108
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu