Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns

A higher-order likelihood-based asymptotic method to obtain inference for the difference between two KS Sharpe ratios when gross returns of an investment are assumed to be lognormally distributed is proposed. Theoretically, our proposed method has On−3/2 distributional accuracy, whereas conventional...

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Main Authors: J. Qi, M. Rekkas, A. Wong
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2020/6751574
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author J. Qi
M. Rekkas
A. Wong
author_facet J. Qi
M. Rekkas
A. Wong
author_sort J. Qi
collection DOAJ
description A higher-order likelihood-based asymptotic method to obtain inference for the difference between two KS Sharpe ratios when gross returns of an investment are assumed to be lognormally distributed is proposed. Theoretically, our proposed method has On−3/2 distributional accuracy, whereas conventional methods for inference have On−1/2 distributional accuracy. Using an example, we show how discordant confidence interval results can be depending on the methodology used. We are able to demonstrate the accuracy of our proposed method through simulation studies.
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institution Kabale University
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spelling doaj-art-7a04ad63c63549d88cec48eac3d419812025-08-20T03:35:48ZengWileyJournal of Probability and Statistics1687-952X1687-95382020-01-01202010.1155/2020/67515746751574Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal ReturnsJ. Qi0M. Rekkas1A. Wong2Institute of Politics and Economics, Nanjing Audit University, 86 West Yushan Road, Nanjing, Jiangsu 211815, ChinaDepartment of Economics, Simon Fraser University, Western Mall 8888 University Drive, Burnaby, British Columbia V5A 1S6, CanadaDepartment of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, CanadaA higher-order likelihood-based asymptotic method to obtain inference for the difference between two KS Sharpe ratios when gross returns of an investment are assumed to be lognormally distributed is proposed. Theoretically, our proposed method has On−3/2 distributional accuracy, whereas conventional methods for inference have On−1/2 distributional accuracy. Using an example, we show how discordant confidence interval results can be depending on the methodology used. We are able to demonstrate the accuracy of our proposed method through simulation studies.http://dx.doi.org/10.1155/2020/6751574
spellingShingle J. Qi
M. Rekkas
A. Wong
Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns
Journal of Probability and Statistics
title Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns
title_full Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns
title_fullStr Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns
title_full_unstemmed Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns
title_short Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns
title_sort inference for the difference of two independent ks sharpe ratios under lognormal returns
url http://dx.doi.org/10.1155/2020/6751574
work_keys_str_mv AT jqi inferenceforthedifferenceoftwoindependentkssharperatiosunderlognormalreturns
AT mrekkas inferenceforthedifferenceoftwoindependentkssharperatiosunderlognormalreturns
AT awong inferenceforthedifferenceoftwoindependentkssharperatiosunderlognormalreturns