Online Program Home
My Program

Abstract Details

Activity Number: 288
Type: Invited
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #318233
Title: Autoregression on Multiple, Adaptively Detected Timescales for the Modeling of High-Frequency Returns
Author(s): Rafal Baranowski and Piotr Fryzlewicz*
Companies: London School of Economics and London School of Economics
Keywords: multiscale modeling ; time series ; change-point ; financial econometrics ; high-frequency finance ; forecasting

Faced with the (difficult) task of predicting high-frequency market movements, it is tempting to entertain the idea of incorporating information from the past behavior of the price process on multiple timescales. However, it is not always a priori clear what timescales are the most relevant in the sense of carrying the most predictive power. With this in mind, we propose a multi-scale autoregressive time series model, in which the quantity of interest (here: the high-frequency return) is explicitly modeled as linearly dependent on its own past averages over unknown time-spans, which we show how to estimate from the data. We show basic probabilistic properties of the model (including how it can mimic white-noise-like behavior), its estimation theory via change-point detection, as well as an application in a high-frequency forecasting exercise, which shows the potential of the new framework.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association