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Robust performance hypothesis testing with smooth functions of population moments


Ledoit, Olivier; Wolf, Michael (2018). Robust performance hypothesis testing with smooth functions of population moments. Working paper series / Department of Economics 305, University of Zurich.

Abstract

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.

Abstract

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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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Working Paper Series > Department of Economics
Dewey Decimal Classification:330 Economics
JEL Classification:C12, C14, C22
Uncontrolled Keywords:Bootstrap, HAC inference, kurtosis, Sharpe ratio, sknewness, variance
Language:English
Date:October 2018
Deposited On:24 Oct 2018 10:39
Last Modified:17 Sep 2019 19:40
Series Name:Working paper series / Department of Economics
Number of Pages:22
ISSN:1664-7041
OA Status:Green
Official URL:http://www.econ.uzh.ch/static/workingpapers.php?id=984

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