Publication Date

2025

Document Type

Dissertation/Thesis

First Advisor

Cheng, Ai-ru

Second Advisor

Anderson, Evan

Degree Name

Ph.D. (Doctor of Philosophy)

Legacy Department

Department of Economics

Abstract

This dissertation consists of two papers. The first paper introduces DCC-SVR, a hybrid Dynamic Conditional Correlation (DCC) and Support Vector Regression (SVR) method of forecasting the covariance matrix. This paper shows that DCC-SVR is able to outperform the traditional methods of DCC and rolling historical on multiple data sets. Performance is shown for both standard GARCH and GJR-GARCH methods. This paper also analyzes performance when dimensions are increased to 49 dimensions and when an application using equal weighted portfolio allocation is used.

The second paper introduces a covariance matrix forecasting method based on copula-GARCH simulated returns. The accuracy of this method is compared against the traditional forecasting methods of Dynamic Conditional Correlation (DCC) and rolling historical. The results found the Clayton vine copula dependency structure performed best and ultimately led to superior forecasts over traditional methods. This is shown to be due to an increased accuracy in modeling the correlation structure of the assets. To ensure robustness of the results, multiple datasets and loss functions are used in the analysis.

Extent

73 pages

Language

en

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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