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Asymptotic and Bayesian Confidence Intervals for Sharpe-Style Weights

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  • Tae-Hwan Kim

Abstract

Sharpe-style regression has become a widely used analytic tool in the financial community. The style regression allows one to investigate such interesting issues as style composition, style sensitivity, and style change over time. All previous methods to obtain the distribution and confidence intervals of the style coefficients are statistically valid only in the special case in which none of the true style weights are zero or one. In practice, it is quite plausible to have zero or one for the values of some style weights. In this article we apply new results and develop a comparable Bayesian method to obtain statistically valid distributions and confidence intervals regardless of the true values of style weights. Copyright 2005, Oxford University Press.

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  • Tae-Hwan Kim, 2005. "Asymptotic and Bayesian Confidence Intervals for Sharpe-Style Weights," Journal of Financial Econometrics, Oxford University Press, vol. 3(3), pages 315-343.
    • Handle: RePEc:oup:jfinec:v:3:y:2005:i:3:p:315-343
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    1. Ter Horst, J.R. & Nijman, T.E. & de Roon, F.A., 2004. "Evaluating style analysis," Other publications TiSEM 8a501733-7a06-4399-8a43-0, Tilburg University, School of Economics and Management.
    2. Laurens Swinkels & Pieter Van Der Sluis, 2006. "Return-based style analysis with time-varying exposures," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 529-552.
    3. ter Horst, Jenke R. & Nijman, Theo E. & de Roon, Frans A., 2004. "Evaluating style analysis," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 29-53, January.
    4. Yunmi Kim & Douglas Stone & Tae-Hwan Kim, 2021. "Testing for structural breaks in return-based style regression models," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(1), pages 61-76, March.
    5. Fricke, Christoph & Fricke, Daniel, 2017. "Vulnerable Funds?," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168209, Verein für Socialpolitik / German Economic Association.
    6. Alessandro Bucciol & Raffaele Miniaci, 2006. "Optimal Asset Allocation Based on Utility Maximization in the Presence of Market Frictions," Working Papers ubs0605, University of Brescia, Department of Economics.
    7. G. Christodoulakis & E. Mamatzakis, 2010. "Return attribution analysis of the UK insurance portfolios," Annals of Finance, Springer, vol. 6(3), pages 405-420, July.
    8. Leif Holger Dietze & Oliver Entrop & Marco Wilkens, 2009. "The performance of investment grade corporate bond funds: evidence from the European market," The European Journal of Finance, Taylor & Francis Journals, vol. 15(2), pages 191-209.
    9. Camponovo, Lorenzo & Scaillet, Olivier & Trojani, Fabio, 2012. "Robust subsampling," Journal of Econometrics, Elsevier, vol. 167(1), pages 197-210.
    10. Stephanos Papadamou & Nikolaos A. Kyriazis & Lydia Mermigka, 2017. "Japanese Mutual Funds before and after the Crisis Outburst: A Style- and Performance-Analysis," IJFS, MDPI, vol. 5(1), pages 1-20, March.
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    12. Pattarin, Francesco & Paterlini, Sandra & Minerva, Tommaso, 2004. "Clustering financial time series: an application to mutual funds style analysis," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 353-372, September.

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