Quantile Tracking Errors (QuTE)

32 Pages Posted: 22 Feb 2020 Last revised: 21 Oct 2021

See all articles by Mike Aguilar

Mike Aguilar

Duke University

Ruyang Chengan

Independent

Anessa Custovic

Cardinal Retirement Planning Inc.

Ziming Huang

Duke University

Date Written: October 18, 2021

Abstract

The tracking error is a ubiquitous tool among active and passive portfolio managers, used widely for fund selection, risk management, and manager compensation. In this paper, we show that traditional measures of tracking error are incapable of detecting variations in higher-order moments (e.g. skewness and kurtosis). As a solution, we introduce a new class of Quantile Tracking Errors (QuTE), which measures deviations in the quantile of return distributions between a tracking portfolio and its benchmark. Through an extensive simulation study, we show that QuTE can detect variations in higher-order moments. We also offer guidance on the granularity of the quantile grid and weighting schemes for the relative importance of various quantiles. A case study illustrates the benefits of QuTE during the Dot Com Bubble and the Great Recession.

Keywords: tracking error, index tracking, portfolio tracking, index fund, quantile

JEL Classification: G11

Suggested Citation

Aguilar, Mike and Chengan, Ruyang and Custovic, Anessa and Huang, Ziming, Quantile Tracking Errors (QuTE) (October 18, 2021). Available at SSRN: https://ssrn.com/abstract=3525171 or http://dx.doi.org/10.2139/ssrn.3525171

Mike Aguilar (Contact Author)

Duke University ( email )

Durham, NC 27708
United States

Ruyang Chengan

Independent ( email )

Anessa Custovic

Cardinal Retirement Planning Inc. ( email )

2530 Meridian Pkway #100
Durham, NC 27713
United States

Ziming Huang

Duke University ( email )

Durham, NC
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
163
Abstract Views
835
Rank
362,153
PlumX Metrics