DOI

10.17077/etd.qlbn3dqf

Document Type

Dissertation

Date of Degree

Summer 2018

Access Restrictions

Access restricted until 08/31/2020

Degree Name

PhD (Doctor of Philosophy)

Degree In

Statistics

First Advisor

Kung-Sik Chan

First Committee Member

Luke Tierney

Second Committee Member

Elias Shiu

Third Committee Member

Johannes Ledolter

Fourth Committee Member

Joyee Ghosh

Abstract

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.

Keywords

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

Pages

xvii, 172 pages

Bibliography

Includes bibliographical references (pages 168-172).

Copyright

Copyright © 2018 Riad Jarjour

Available for download on Monday, August 31, 2020

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