Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/192914
Authors: 
Ledoit, Olivier
Wolf, Michael
Year of Publication: 
2018
Series/Report no.: 
Working Paper 305
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.
Subjects: 
Bootstrap
HAC inference
kurtosis
Sharpe ratio
sknewness
variance
JEL: 
C12
C14
C22
Persistent Identifier of the first edition: 
Document Type: 
Working Paper
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