An analysis of tracking errors within European exchange traded funds

Abstract

This dissertation aims to identify whether exchange traded funds (ETFs) perform positively in tracking their underlying benchmarks. This was carried out by calculating the tracking errors using different methods outlined by Frino and Gallagher (2002). More specifically this dissertation focuses on European ETFs, were a total of 29 ETFs were chosen and split into five different categories, in order to provide better insight into different types of ETFs. Data for the 29 ETFs as well as their underling benchmarks were extracted from the period of January 2009 to December 2018, spanning over a ten-year period. Such findings suggest that the selected ETFs on average, have the ability to track their underlying benchmarks efficiently. This was shown through the tracking error results obtained from the three different methods, which resulted in significantly low tracking errors. Furthermore, positive results were obtained from the regression analysis, which indicated that the ETFs performed positively in tracking their underlying benchmarks. Results also show that volatility levels and tracking errors are positively related. The implication from this analysis was that Short & Leveraged ETFs suffered from the highest tracking errors, thus it may be implied that such ETFs are not optimal assets to be invested in, as compared to other ETFs.