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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Robust performance hypothesis testing with smooth functions of population moments
Organization Unit
  • Olivier Ledoit
  • Michael Wolf
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 305
ISSN 1664-7041
Number of Pages 22
Date 2018
Abstract Text Applied researchers often want to make inference for the difference of a given performance measure for two investment strategies. In this paper, we consider the class of performance measures that are smooth functions of population means of the underlying returns; this class is very rich and contains many performance measures of practical interest (such as the Sharpe ratio and the variance). Unfortunately, many of the inference procedures that have been suggested previously in the applied literature make unreasonable assumptions that do not apply to real-life return data, such as normality and independence over time. We will discuss inference procedures that are asymptotically valid under very general conditions, allowing for heavy tails and time dependence in the return data. In particular, we will promote a studentized time series bootstrap procedure. A simulation study demonstrates the improved finite-sample performance compared to existing procedures. Applications to real data are also provided.
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Keywords Bootstrap, HAC inference, kurtosis, Sharpe ratio, sknewness, variance, Bootstrap-Statistik, Kurtosis, Schiefe, Varianz, Investition, Strategie