Center for Industrial Mathematics and Statistics » REU 2014 – Estimating Liquidity Risk Using Exchange Traded Funds

Hannah Li

Evan Witz

Claire Kelling

Rachel Crowell

Overview

Industrial Sponsor: Dr. Luis Roman and Dr. Ritripua Samanta - State Street Global Advisors

Faculty Advisor: Marcel Blais

When valuing a portfolio of risky assets, portfolio managers face the issue that the market value of a portfolio may differ significantly from the short-term liquidation value of the portfolio. In short, a collection of assets may not be worth its quoted market value if the portfolio manager has to liquidate the portfolio over a short-term time horizon. This is a type of liquidity risk, one that many financial institutions, such as State Street Global Advisors, face. The financial crisis of 2008 illustrated that this is type of risk is dangerous to ignore, which has lead to the current need in industry to be able to model and predict liquidity premiums and their fluctuations over time. The metrics currently used in financial industry for liquidity employ proxies which may involve other risk factors, making a liquidity premium difficult to isolate. The proposed model isolates liquidity risk from other factors by forming a portfolio based on buying Exchange Traded Funds (ETFs), which trade alongside stocks on exchanges, and shorting the underlying basket of securities, which are typically less liquid than the ETFs, using the weighting system employed by the ETF issuer. Essentially we are buying and selling the same set of securities, and the difference between the price of the ETF and the value of the basket of underlying securities should be purely due to a difference in liquidity. We work with the ETF called JNK, which is based on bonds with poor credit ratings as issued by Fitch, Moody's, and Standard & Poor's. We assess and implement the model provided by Chacko et al. in An Index Based Measure of Liquidity by analyzing empirical data of the JNK and the underlying securities. We will then have the opportunity to suggest extensions or modifications of the model and compare our models with other common methods of liquidity modeling.