Stefano Giglio

Stefano Giglio, Yale School of Management

Abstract

Data snooping is a major concern in empirical asset pricing. By exploiting the "blessings of dimensionality" we develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data-snooping. We rst develop alpha test statistics that are asymptotically valid, allow for weak dependence in the cross-section, and are robust to the possibility of omitted factors. We then combine them in a multiple-testing procedure that ensures that the rate of false discoveries is ex-ante bounded below a prespecied 5% level. We also show that this method can detect all positive alphas with reasonable strength. Our procedure is designed for high-dimensional settings and works even when the number of tests is large relative to the sample size, as in many nance applications. We illustrate the empirical relevance of our methodology in the context of hedge fund performance (alpha) evaluation. We nd that our procedure is able to select - among more than 3,000 available funds { a subset of funds that displays superior in-sample and out-of-sample performance compared to the funds selected by standard methods.

Bio:

Professor Giglio’s research interests span several topics, including asset pricing, macroeconomics, and real estate, with a particular focus on volatility risk and on the term structure of asset prices across markets.

Before joining Yale, Professor Giglio was an Associate Professor of Finance at the University of Chicago Booth School of Business. He has been awarded several prizes, including the Fama-DFA Prize for the Best Paper in the Journal of Financial Economics, the Jacob Gold & Associates Best Paper Prize, and the UBS Global Asset Management Award for Research in Investments. His work has been featured in several news outlets, including Forbes and the Economist.