Modeling tick-by-tick realized correlations

Audrino, F. & Corsi, F. (2010). Modeling tick-by-tick realized correlations. Computational Statistics and Data Analysis, 54(11), pp. 2372-2382. doi: 10.1016/j.csda.2009.09.033

[img]
Preview
PDF - Accepted Version
Download (341kB) | Preview

Abstract

A tree-structured heterogeneous autoregressive (tree-HAR) process is proposed as a simple and parsimonious model for the estimation and prediction of tick-by-tick realized correlations. The model can account for different time and other relevant predictors’ dependent regime shifts in the conditional mean dynamics of the realized correlation series. Testing the model on S&P 500 Futures and 30-year Treasury Bond Futures realized correlations, empirical evidence that the tree-HAR model reaches a good compromise between simplicity and flexibility is provided. The model yields accurate single- and multi-step out-of-sample forecasts. Such forecasts are also better than those obtained from other standard approaches, in particular when the final goal is multi-period forecasting.

Item Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis Volume 54, Issue 11, 1 November 2010, Pages 2372–2382, http://dx.doi.org/10.1016/j.csda.2009.09.033
Uncontrolled Keywords: High frequency data, Realized correlation, Stock–bond correlation, Tree-structured models, HAR, Regimes
Subjects: H Social Sciences > HB Economic Theory
Divisions: School of Social Sciences > Department of Economics
Related URLs:
URI: http://openaccess.city.ac.uk/id/eprint/4436

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics