Principal Components Analysis (PCA) is an important mathematical technique widely used in the world of quantitative finance. The ultimate goal of this paper is to construct a portfolio with hedging positions, which is able to outperform the SPY benchmark in terms of the Sharpe ratio. Mathematical techniques implemented in this paper besides principle component analysis are the Sharpe ratio, ARMA, ARCH, GARCH, ACF, and Markowitz methodology. Information about these mathematical techniques is listed in the introduction section. Through conducting in sample analysis, out sample analysis, and back testing, it is demonstrated that the quantitative approach adopted in this paper, such as principle component analysis, can be used to find the major driving factor causing movements of a portfolio, and we can perform a more effective portfolio analysis by using principle component analysis to reduce the dimensions of a financial model.

Creator

• Chen, Huanting

Contributori

• Blais, Marcel Y.

Degree

• MS

Unit

• Mathematical Sciences

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-080614-144242

Parola chiave

• Principle Component Analysis

Advisor

• Blais, Marcel Y.

Defense date

• 2014-08-06

Year

• 2014

Date created

• 2014-08-06

Resource type

• Report

Rights statement

• In Copyright

Ultima modifica

• 2021-01-28