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Asymptotic and Bayesian Confidence Intervals for Sharpe Style Weights

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  • Kim, Tae-Hwan
  • White, Halbert
  • Stone, Douglas

Abstract

Sharpe style regression has become a widespread 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 paper we apply new results of Andrews (1997a, 1999) and develop a comparable Bayesian method to obtain statistically valid distributions and confidence intervals regardless of the true values of style weights.

Suggested Citation

  • Kim, Tae-Hwan & White, Halbert & Stone, Douglas, 2000. "Asymptotic and Bayesian Confidence Intervals for Sharpe Style Weights," University of California at San Diego, Economics Working Paper Series qt5h98h28m, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt5h98h28m
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    References listed on IDEAS

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    1. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
    2. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
    3. Brown, Stephen J. & Goetzmann, William N., 1997. "Mutual fund styles," Journal of Financial Economics, Elsevier, vol. 43(3), pages 373-399, March.
    4. D. N. Graham, 1961. "Discussion," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 9(1), pages 54-55, March.
    5. Donald W.K. Andrews, 1997. "Estimation When a Parameter Is on a Boundary: Theory and Applications," Cowles Foundation Discussion Papers 1153, Cowles Foundation for Research in Economics, Yale University.
    6. Geweke, John, 1986. "Exact Inference in the Inequality Constrained Normal Linear Regression Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(2), pages 127-141, April.
    7. Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
    8. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," The Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
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    Cited by:

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    3. 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.
    4. 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.
    5. 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.
    6. Camponovo, Lorenzo & Scaillet, Olivier & Trojani, Fabio, 2012. "Robust subsampling," Journal of Econometrics, Elsevier, vol. 167(1), pages 197-210.
    7. 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.
    8. 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.
    9. 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.
    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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