Document Type


Date of Degree

Summer 2018

Access Restrictions

Access restricted until 08/31/2020

Degree Name

PhD (Doctor of Philosophy)

Degree In


First Advisor

Chan, Kung-Sik

First Committee Member

Tierney, Luke

Second Committee Member

Shiu, Elias

Third Committee Member

Ledolter, Johannes

Fourth Committee Member

Ghosh, Joyee


The dynamic conditional correlation (DCC) model and its variants have been widely used in modeling the volatility of multivariate time series, with applications in portfolio construction and risk management. While popular for its simplicity, the DCC uses only two parameters to model the correlation dynamics, regardless of the number of assets. The flexible dynamic conditional correlation (FDCC) model attempts to remedy this by grouping the stocks into various clusters, each with its own set of parameters. However, it assumes the grouping is known apriori.

In this thesis we develop a systematic method to determine the number of groups to use as well as how to allocate the assets to groups. We show through simulation that the method does well in identifying the groups, and apply the method to real data, showing its performance. We also develop and apply a Bayesian approach to this same problem.

Furthermore, we propose an instantaneous measure of correlation that can be used in many volatility models, and in fact show that it outperforms the popular sample Pearson's correlation coefficient for small sample sizes, thus opening the door to applications in fields other than finance.


clustering, correlation, garch, stocks, time series, volatility


xvii, 172 pages


Includes bibliographical references (pages 168-172).


Copyright © 2018 Riad Jarjour

Available for download on Monday, August 31, 2020