Measuring Liquidity of Fixed-Income Assets

Abstract

This thesis will dissect and analyze market liquidity as well as the liquidity of fixed income assets. The goal is to create a reliant and accurate way to predict the liquidity of a certain fixed income security has. Simply put, liquidity is how easily can an asset be sold without impacting its market price. This definition changes slightly as we focus on the different types of liquidity. However, whether we are looking at market liquidity or the liquidity of a portfolio, we are still trying to measure the ease of liquidating. The Covid-19 pandemic also sheds light onto the importance of liquidity and how our market can respond to an unpredictable negative shock. Seeing how the market reacts and understanding the movement of the market, more specifically the fixed income market, will be interesting since there has not been much literature in the past and yet is a topic that has been gaining interest in the last few years. There are many pieces of information that can help one to understand how liquid an asset is, such as the bid-ask spread, the volume of a security, and the age of a security. There have been attempts to create a model that measures liquidity by some data companies, but are very basic calculations and do not encompass all of the different factors that can be affecting liquidity. Many of the methods that are out for financial institutions to use have one major setback: they can only really measure liquidity within an asset class. The goal of my thesis would be to create an all encompassing method using basic regressions and machine learning techniques that would measure asset liquidity of fixed income assets and create a simplistic number representative of liquidity that can be used relative to other assets in different classes.