Title page for ETD etd-050212-104256

Document Type masters report

Author Name Yan, Lu

Email Address yanluwendy at wpi.edu

URN etd-050212-104256

Title Risk Management Project

Degree MS

Department Mathematical Sciences

Advisors
Blais, Marcel Y., Advisor

Keywords
Risk Management
Value-at-risk
Expected Shortfall
ARMA-GARCH
Chi-square test
AIC
BIC
Portfolio Optimization

Date of Presentation/Defense 2012-04-30

Availability unrestricted

Abstract
In order to evaluate and manage portfolio risk, we separated this project into three sections. In the first section we constructed a portfolio with 15 different stocks and six options with different strategies. The portfolio was implemented in Interactive Brokers and rebalanced weekly through five holding periods. In the second section we modeled the loss distribution of the whole portfolio with normal and student-t distributions, we computed the Value-at-Risk and expected shortfall in detail for the portfolio loss in each holding week, and then we evaluated differences between the normal and student-t distributions. In the third section we applied the ARMA(1,1)-GARCH(1,1) model to simulate our assets and compared the polynomial tails with Gaussian and t-distribution innovations.

Files
lyan.pdf

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Document Type	masters report
Author Name	Yan, Lu
Email Address	yanluwendy at wpi.edu
URN	etd-050212-104256
Title	Risk Management Project
Degree	MS
Department	Mathematical Sciences
Advisors	Blais, Marcel Y., Advisor
Keywords	Risk Management Value-at-risk Expected Shortfall ARMA-GARCH Chi-square test AIC BIC Portfolio Optimization
Date of Presentation/Defense	2012-04-30
Availability	unrestricted
Abstract In order to evaluate and manage portfolio risk, we separated this project into three sections. In the first section we constructed a portfolio with 15 different stocks and six options with different strategies. The portfolio was implemented in Interactive Brokers and rebalanced weekly through five holding periods. In the second section we modeled the loss distribution of the whole portfolio with normal and student-t distributions, we computed the Value-at-Risk and expected shortfall in detail for the portfolio loss in each holding week, and then we evaluated differences between the normal and student-t distributions. In the third section we applied the ARMA(1,1)-GARCH(1,1) model to simulate our assets and compared the polynomial tails with Gaussian and t-distribution innovations.
Files	lyan.pdf