SSRN Author: Nicolas ParisNicolas Paris SSRN Content
https://www.ssrn.com/author=3157801
https://www.ssrn.com/rss/en-usThu, 31 Oct 2019 01:02:24 GMTeditor@ssrn.com (Editor)Thu, 31 Oct 2019 01:02:24 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Omega and Sharpe RatioOmega ratio, defined as the probability-weighted ratio of gains over losses at a given level of expected return, has been advocated as a better performance indicator compared to Sharpe and Sortino ratio as it depends on the full return distribution and hence encapsulates all information about risk and return. We compute Omega ratio for the normal distribution and show that under some distribution symmetry assumptions, the Omega ratio is oversold as it does not provide any additional information compared to Sharpe ratio. Indeed, for returns that have elliptic distributions, we prove that the optimal portfolio according to Omega ratio is the same as the optimal portfolio according to Sharpe ratio. As elliptic distributions are a weak form of symmetric distributions that generalized Gaussian distributions and encompass many fat tail distributions, this reduces tremendously the potential interest for the Omega ratio.
https://www.ssrn.com/abstract=3469888
https://www.ssrn.com/1837359.htmlWed, 30 Oct 2019 09:33:45 GMTNew: Testing Sharpe Ratio: Luck or Skill?Sharpe ratio (sometimes also referred to as information ratio) is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the (excess) net return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and the volatilities are unknown numbers and need to be estimated statistically. This means that the Sharpe ratio used by funds is likely to be error prone because of statistical estimation errors. In this paper, we provide various tests to measure the quality of the Sharpe ratios. By quality, we are aiming at measuring whether a manager was indeed lucky of skillful. The test assesses this through the statistical significance of the Sharpe ratio. We not only look at the traditional Sharpe ratio but also compute a modified Sharpe insensitive to used Capital. We provide various statistical tests that can be used to precisely quantify the fact that the Sharpe is statistically ...
https://www.ssrn.com/abstract=3391214
https://www.ssrn.com/1794773.htmlThu, 06 Jun 2019 21:30:07 GMTNew: BCMA-ES II: Revisiting Bayesian CMA-ESThis paper revisits the Bayesian CMA-ES and provides updates for normal Wishart. It emphasizes the difference between a normal and normal inverse Wishart prior. After some computation, we prove that the only difference relies surprisingly in the expected covariance. We prove that the expected covariance should be lower in the normal Wishart prior model because of the convexity of the inverse. We present a mixture model that generalizes both normal Wishart and normal inverse Wishart model. We finally present various numerical experiments to compare both methods as well as the generalized method.
https://www.ssrn.com/abstract=3365453
https://www.ssrn.com/1785363.htmlMon, 06 May 2019 16:13:38 GMT