About

Michael Aguilar is a financial economist operating at the intersection of macroeconomics, investment strategy, and quantitative methods. He has extensive teaching experience, having taught at UNC Chapel Hill in the Economics Department and the Kenan Flagler Business School, and expanded his teaching duties to Duke University within Fuqua's MQM program in 2018. At Duke, he has worked with students across various programs including the MSQM, MMS, WEMBA, and GEMBA, as well as undergraduate students in the Economics Department. His research focus and private practice overlap with his teaching interests, and he tends to view the world from a 'top down' perspective, applying macro, finance, and quantitative skills to roles such as Chief Investment Officer, Research Director, and Founder/President of several companies. He leverages his professional experiences to enhance his teaching, emphasizing experiential education through nontraditional settings like the Fed Challenge, Credential in Quantitative Financial Economics, and the Fiscal Challenge.

Research topics

• Computer Science
• Artificial Intelligence
• Econometrics
• Finance
• Economics
• Statistics
• Data Mining
• Political Science
• Mathematics
• Microeconomics
• Business
• Actuarial science
• Financial economics

Selected publications

• Creating better tracking portfolios with quantiles

  Investment Management and Financial Innovations · 2022 · 2 citations

  1st authorCorresponding
  • Computer Science
  • Data Mining
  • Computer Science

  Tracking error is a ubiquitous tool among active and passive portfolio managers, widely used for fund selection, risk management, and manager compensation. This paper shows that traditional measures of the tracking error are incapable of detecting variations in skewness and kurtosis. As a solution, this paper introduces a new class of Quantile Tracking Errors (QuTE), which measures differences in the quantiles of return distributions between a tracking portfolio and its benchmark. Through an extensive simulation study, this paper shows that QuTE is six times more sensitive than traditional tracking measures to skewness and three times more sensitive to kurtosis. The QuTE statistic is robust to various calibrations and can easily be customized. By using the QuTE tracking measure during the Dot Com bubble and the Great Recession, this paper finds differences between the DIA and its benchmark, the DJIA, that otherwise would have gone undetected. Quantile based tracking provides a robust method for relative performance measurement and index portfolio construction.

  PublisherDOI

• Moment condition tests for heavy tailed time series

  Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2021-08-14

  articleOpen accessSenior author

  We develop an asymptotically chi-squared test statistic for testing moment conditions E[mt(Θ0)] = 0 where scalar components of mt(Θ0) may have an infinite variance and mt(Θ0) may be weakly dependent. In general E[mt(Θ0)] need not exist under the alternative. A variety of tests can be heavy-tail robustified by our method, including white noise, GARCH affects, omitted variables, order selection, functional form, causation, volatility spillover and over-identification. The test statistic is derived from a tail-trimmed sample version of the moments evaluated at a consistent plug-in ^ΘT for Θ0. Depending on the test in question ^ΘT may be any consistent estimator like QML, LAD, GMM, and Empirical Likelihood as well as robust estimators like Least Trimmed Squares, Least Absolute Weighted Deviations, and Generalized Method of Tail-Trimmed Moments. Simple rules of thumb for selecting the trimming fractiles are presented, and in many cases when mt(Θ0) has infinite variance components the fractiles and/or ^Θ T can be chosen to ensure ^ΘT does not influence the test statistic's limit distribution. Thus, in heavy tailed cases ^ΘT does not need to have a Gaussian limit. We apply our statistic to tests of white noise, omitted variables and volatility spillover and find it obtains correct empirical size, while conventional tests exhibit sharp distortions.

  PublisherOA PDFDOI

• Portfolio Optimization Without Optimization

  SSRN Electronic Journal · 2021-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Fiscal Challenge: An Experiential Exercise in Policy Making

  Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2021-09-08

  articleOpen accessSenior author

  In this article we introduce a pedagogical innovation that is designed to enhance our students' understanding of fiscal policy, in general, and the national debt and deficit, in particular. The innovation leverages the educational advantages offered through a competitive environment by pitting teams of students against one another with the goal of devising the best plan to put the U.S. on a sustainable fiscal path. The current incarnation of the competition, which is referred to as the Fiscal Challenge (FC), confronts the students with the specific task of stabilizing the U.S. Federal Debt to GDP ratio. This specific task may change from one competition season to the next, potentially adjusting with the economic climate and interests of the participants. The FC currently is being implemented as a nationwide, inter-university, extracurricular activity; however, it can easily be customized to fit within a traditional classroom setting.

  PublisherOA PDFDOI

• Quantile Tracking Errors (QuTE)

  SSRN Electronic Journal · 2020

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science
  PublisherDOI

• A Practical Method for Sharpening Estimates of Industry Equity Capital Costs

  SSRN Electronic Journal · 2020

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Political Science
  PublisherDOI

• Essays in financial econometrics: GMM and conditional heteroscedasticity

  Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2019-08-15

  articleOpen access1st authorCorresponding

  This dissertation consists of three papers in the field of financial econometrics. In the first paper, I use a factor structure to model a system of conditionally heteroscedastic asset returns. In the second paper, I illustrate how standard asymptotic results for gmm estimators may be maintained even in the face of moment conditions with infinite variance. In the third paper, I describe a test to distinguish garch from stochastic volatility models.

  PublisherDOI

• The Impact of Internal Migration on Personal Loan Default in China

  SSRN Electronic Journal · 2018-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Crowdsourcing Economic Forecasts

  SSRN Electronic Journal · 2018-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• The Dynamics of REIT Pricing Efficiency

  Real Estate Economics · 2017-07-24 · 20 citations

  article1st authorCorresponding

  Abstract We study the dynamics of pricing efficiency in the equity REIT market from 1993 to 2014. We measure pricing efficiency at the firm level using variance ratios calculated from quote midpoints in the TAQ database. We find four main results. First, on average, the market is efficient, with variance ratios close to one. However, in any given year, there is considerable cross‐sectional variation in variance ratios, suggesting at least some firms are priced inefficiently. Second, higher institutional ownership by active institutional investors is related to better pricing efficiency, while passive ownership does not reduce pricing efficiency. Third, REITs that are included in the S&P 500 and S&P 400 are priced more efficiently than other REITs. For the S&P 500 firms, we find evidence that this was purely driven by sample selection, while for S&P 400 firms, we find evidence that it is inclusion in the index that drives efficiency. Finally, we find evidence that firm investment, analyst coverage and debt capital raising activity can influence pricing efficiency.

  PublisherDOI

Frequent coauthors

• Jonathan B. Hill

  University of North Carolina at Chapel Hill

  6 shared

• Anessa Custovic

  4 shared

• Daniel Soques

  University of North Carolina Wilmington

  4 shared

• Ruyang Chengan

  3 shared

• Robert A. Connolly

  University of Florida

  2 shared

• Ziming Huang

  Duke University

  2 shared

• Walter I. Boudry

  Southern Methodist University

  2 shared

• Tim Lieuwen

  Georgia Institute of Technology

  1 shared