I read a paper from Ledoit and Wolf that proposes a method to compare two Sharpe ratios and a paper from White that proposes a method to compare $n$ trading rules.

My question is: Can we use White's method to compare two Sharpe ratios? I prefer this method because it's computationally simpler.

  • $\begingroup$ Did you see the source code provided by one of the authors of the Ledoit and Wolf paper? $\endgroup$
    – Bob Jansen
    Mar 13, 2012 at 19:18
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    $\begingroup$ The point of White is to adjust for multiplicity. Why use it to compare only two Sharpe ratios? Or maybe you want to use it for $n >> 2$ Sharpe ratios? $\endgroup$
    – James
    Sep 12, 2014 at 14:28
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    $\begingroup$ @ColinTBowers apparently it was under patent until Jan 2017, as a simple google search would have revealed: patents.google.com/patent/US5893069A/en?oq=5893069 . And yes, Hansen's loglog trick is certainly an improvement on WRC. Can I ask what the "tests proposed in some recent publications" are? $\endgroup$ Feb 18, 2020 at 19:15
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    $\begingroup$ ah, thanks. I only recalled White's patent because I remember feeling subversive when implementing it. I too reflexively doubt patentability of 'obvious' uses of known methods, but I also worked for a company that lost +500M in a patent case for using Kalman Filters (!). A recent survey of several methods for MHT correction on Sharpe is Pav, 2019. Again, none of these are required for $n=2$ strategies. $\endgroup$ Feb 19, 2020 at 1:09
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    $\begingroup$ @ColinTBowers Yes, the CLT/Delta method analysis was proposed by Jobson & Korkie, later turned into F-test by Leung & Wong, and a $\chi^2$ test by Wright et al. $\endgroup$ Feb 20, 2020 at 18:30

1 Answer 1


As James has pointed out in the comments, White's Reality Check is specifically designed to control the family-wise error rate given $k > 2$ statistics. The theory does not depend on $k$ asymptotics, so there is nothing invalid about using White's Reality Check for $2$ statistics, but in practice there would be little point to doing this. Further, as stevo`america points out above, the Reality check had a patent on it until two years ago - whether it would be enforced in a court case though is another question entirely...

In particular, for $k=2$, it is fairly straightforward to construct a simple statistical test for the difference in two Sharpe ratios. Presumably there is some insight in Ledoit and Wolf's paper that makes their statistic superior to what I am about to suggest. Also, see stevo`americas comments on the question for references to some other sophisticated testing measures. But if what you're after is simplicity, then the following is still perfectly valid:

Let $R_{1,t}$, and $R_{2,t}$ denote returns on the two assets of interest. In this framework, I define the Sharpe ratio: \begin{equation} S_1 = \frac{\mathbb{E} R_{1,t}}{\sqrt{\mathbb{V} R_{1,t}}} \end{equation} For any random variable $X_t$ the sample mean is defined: \begin{equation} \bar{X} = \frac{1}{T} \sum_{t=1}^T X_t \end{equation} Let: \begin{equation} \bar{\sigma}_1 = \sqrt{\frac{1}{T} \sum_{t=1}^T (R_{1,t} - \bar{R}_1)^2} \end{equation} A natural estimator for $S_1$ is: \begin{equation} \hat{S}_1 = \frac{\bar{R}_1}{\bar{\sigma}_1} \end{equation} I assume suitable regularity conditions on $R_{1,t}$ such that $\bar{R}_1 \overset{\mathbb{P}}{\rightarrow} \mathbb{E} R_{1,t}$, $\sqrt{T} \bar{R}_1 \overset{d}{\rightarrow} \mathcal{N}$, and $\bar{\sigma}_1 \overset{\mathbb{P}}{\rightarrow} \sqrt{\mathbb{V}(R_{1,t})}$ (e.g. weak dependence and suitably bounded moments). By Slutsky's theorem, these conditions are sufficient for: \begin{equation} \hat{S}_1 \overset{\mathbb{P}}{\rightarrow} S_1 \end{equation} and note that by Cramer's theorem: \begin{equation} \sqrt{T} \bar{S}_1 = \frac{\sqrt{T} \bar{R}_1}{\bar{\sigma}_1} \overset{d}{\rightarrow} \mathcal N \end{equation} since the numerator is converging in distribution to a Normal, and the denominator is converging in probability to a constant strictly greater than $0$.

So we have a CLT for our statistic. For the purposes testing a difference in two statistics, it is easier if our statistic can be phrased as a single sample mean. This is straightforward. Let: \begin{equation} Y_{1,t} = (\bar{\sigma}_1)^{-1} R_{1,t} , \end{equation} where it is worth emphasizing that it immediately follows that: \begin{equation} \hat{S}_1 = \bar{Y}_1 . \end{equation} Incorporating the second asset, we now define: \begin{equation} d_t = Y_{1,t} - Y_{2,t} . \end{equation} The theory thus far is sufficient to show that under: \begin{equation} H_0 : S_1 = S_2 , \end{equation} we have: \begin{equation} \bar{d} \overset{d}{\rightarrow} \mathcal{N}(0, \alpha) . \end{equation} So we've literally transformed the problem into testing whether a sample mean is equal to zero, with a CLT existing for the sample mean. If you think $d_t$ exhibits time-series dependence, then you will need to estimate $\alpha$ using a HAC estimator, or else you could just bootstrap the statistic. Both are likely to give you similar outcomes. If you aren't worried about time-series dependence then just estimate $\alpha$ using the sample standard deviation of $d_t$ over $\sqrt{T}$.


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