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  • 标题:Inference for the Sharpe Ratio Using a Likelihood-Based Approach
  • 本地全文:下载
  • 作者:Ying Liu ; Marie Rekkas ; Augustine Wong
  • 期刊名称:Journal of Probability and Statistics
  • 印刷版ISSN:1687-952X
  • 电子版ISSN:1687-9538
  • 出版年度:2012
  • 卷号:2012
  • DOI:10.1155/2012/878561
  • 出版社:Hindawi Publishing Corporation
  • 摘要:The Sharpe ratio is the prominent risk-adjusted performance measure used by practitioners. Statistical testing of this ratio using its asymptotic distribution has lagged behind its use. In this paper, highly accurate likelihood analysis is applied for inference on the Sharpe ratio. Both the one- and two-sample problems are considered. The methodology has distributional accuracy and can be implemented using any parametric return distribution structure. Simulations are provided to demonstrate the method's superior accuracy over existing methods used for testing in the literature.
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