Paulo Ferreira Naibert
Original: 2019 October 13.
Current: 05, agosto, 2020
This Repository contains functions to replicate Oliver Ledoit and Wolf (2008), Oliver Ledoit and Wolf (2011), and Olivier Ledoit and Wolf (2018).
Michael Wolf provides R codes for those two papers on his webpage, in the publications section.
What is found here is merely an edit of those codes.
If you find any bug, or technical error, please contact me.
On Oliver Ledoit and Wolf (2008), Oliver Ledoit and Wolf (2011), and Olivier Ledoit and Wolf (2018), the authors provide methods to test the hypothesis of difference in the Sharpe Ratio and the Variance of two investment strategies. The papers can be found in the following links:
SR Paper, SR WP, Variance Paper, Variance WP, Generalized WP.
Also, I made a summary of those papers here
The functions can be found here. They are edits of the Sharpe.RData and Var.RData files that can be found in Michael Wolf's website with some additions and modifications.
I didn't have enought time to modify the block.size.calibrate() functions.
For the data, I simpy saved the returns data of Sharpe.RData as a .RDS file. Loading rets.RDS should result in a list object on the R Software (R Core Team 2019) .
See DEMO files.
- Document Functions (R and html nb)
I own NONE of the rights to the codes. I simply edited the already existing codes in Michael Wolf's Webpage. I also offer NO support for the functions. They are presented "AS IS".
I would like to thank Michael Wolf for providing the original functions on his webstite.
Ledoit, Oliver, and Michael Wolf. 2008. “Robust performance hypothesis testing with the Sharpe ratio.” Journal of Empirical Finance 15 (5): 850–59.
———. 2011. “Robust performance hypothesis testing with the Variance.” Wilmott Magazine, no. 55 (September): 86–89.
Ledoit, Olivier, and Michael Wolf. 2018. “Robust Performance Hypothesis Testing with Smooth Functions of Population Moments.” Working Paper Series 305. Department of Economics, University of Zurich.
R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.