Robust Performance Hypothesis Testing with the Sharpe Ratio

By Olivier Ledoit, Michael Wolf. 2008

875 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/70129c93fc9d8590b6aa079026ba59967d199e52

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Citations in the corpus

Dominating estimators for minimum-variance portfolios (2010, methodology): “…why we apply the circular moving-blocks bootstrap.12 Block-bootstrap procedures guarantee that the considered hypothesis tests remain asymptotically valid even for serially dependent time series (Politis, 2003) and have become popular instruments in financial data analysis (Ledoit and Wolf, 2008).”
Improving Portfolio Selection Using Option-Implied Volatility and Skewness (2013, methodology): “For a nice discussion of the application of other bootstrapping methods to tests of differences in portfolio performance, see Ledoit and Wolf (2008). we compute the time series of net returns r̃strategyt+∆t for a given strategy and the benchmark: r̃strategyt+∆t = r strategy t+∆t − N∑ j=1…”
Robust Forecasting of Dynamic Conditional Correlation GARCH Models (2013, result): “Similar results were obtained when testing using the bootstrap procedure of Ledoit and Wolf (2008).”
Stock Return Serial Dependence and Out-of-Sample Portfolio Performance (2014, methodology): “Merton (1980) explains the theoretical reason why it is much more difficult to get precise estimates of first moments than second moments.”
On the sources of uncertainty in exchange rate predictability (2018, positive): “In light of the preceding discussion, we construct bootstrapped critical values for the onesided DMW test in the spirit of Rogoff and Stavrakeva (2008). Given our search over multiple predictors, however, we modify their procedure to account for data-mining, following Inoue and Kilian (2005) and Rap”
Demystifying Time‐Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations (2020, positive): “However, the Ledoit and Wolf (2008) p-value shows that these Sharpe ratios are not statistically indistinguishable from each other.”
Do socially (ir)responsible investments pay? New evidence from international ESG data (2016, background): “…non-normal distributions (see Schuhmacher & Eling, 2011, 2012) and the significance of performance differences can be tested using state-of-the-art bootstrap methods that are robust to typical features of financial time series like non-normality and serial correlation (see Ledoit & Wolf, 2008).”
The coefficient of variation asymptotic distribution in the case of non-iid random variables (2009, methodology): “gif"/> considering an HAC estimator for Σ and we apply the 28 studentized bootstrap method to test the nullity of Δ.”
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading (2017, background): “, SR, which has been widely used in many trading related works [42], [43].”
Improving Mean Variance Optimization through Sparse Hedging Restrictions (2015, methodology): “We test the null of no difference in Sharpe ratios by constructing two-sided bootstrap intervals for the difference, using the studentized circular block bootstrap of Ledoit and Wolf (2008) with an optimal block size.”
Size Matters: Optimal Calibration of Shrinkage Estimators for Portfolio Selection (2011, methodology): “Finally, to measure the statistical significance of the difference between the adjusted Sharpe ratios, we use the stationary bootstrap of Politis and Romano (1994) with B=1000 bootstrap samples and block size b=117.”
A Network Approach to Portfolio Selection (2016, methodology): “The p-values are computed following the procedure in Ledoit and Wolf (2008) and Ledoit and Wolf (2011) based on a studentized circular block bootstrap with block size equal to 5 and number of bootstrap samples equal to 5000.”