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A Capital Asset Pricing Model’s (CAPM’s) Beta Estimation in the Presence of Normality and Non-normality Assumptions

Received: 10 April 2017     Accepted: 14 April 2017     Published: 16 June 2017
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Abstract

This study describes the approach for estimating the beta-risk of the Capital Asset Price Model (CAPM) when the normality (Gaussian) assumption of both the error term and the excess return on an asset holds, and also when their normality assumption is violated or failed due to outliers or excessive skewness and excessive kurtosis. The student-t distribution was used as an alternative distribution to capture these anomalies. The monthly All-share Index (ASI) of 12 crucial Market Portfolios / Sectors derived from Nigeria Stock Exchange (NSE) were subjected to both the Gaussian error innovation and Student-t error innovation in this study. However, it was noted that estimates of portfolios’ beta-risk and its standard error for Gaussian and student-t were approximately the same when the sector follows a normal distribution while the standard errors of portfolio beta-risk estimates will be smaller under student-t innovation than that of Gaussian innovation when the sector does not follow normal distribution due to these anomalies. Furthermore, it was discovered that building & construction, manufacturing, quarry & mining, communication, transportation, education and utilities sectors have been having lower volatility, that is, in boosting the economy over the last 15 years.

Published in International Journal of Finance and Banking Research (Volume 3, Issue 3)
DOI 10.11648/j.ijfbr.20170303.12
Page(s) 44-52
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Beta-Risk, Capm, Expected Returns, Gaussian Innovation, Student-T Innovation, Systematic Risk

References
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Cite This Article
  • APA Style

    Ezekiel Oseni, Razak Olawale Olanrewaju. (2017). A Capital Asset Pricing Model’s (CAPM’s) Beta Estimation in the Presence of Normality and Non-normality Assumptions. International Journal of Finance and Banking Research, 3(3), 44-52. https://doi.org/10.11648/j.ijfbr.20170303.12

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    ACS Style

    Ezekiel Oseni; Razak Olawale Olanrewaju. A Capital Asset Pricing Model’s (CAPM’s) Beta Estimation in the Presence of Normality and Non-normality Assumptions. Int. J. Finance Bank. Res. 2017, 3(3), 44-52. doi: 10.11648/j.ijfbr.20170303.12

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    AMA Style

    Ezekiel Oseni, Razak Olawale Olanrewaju. A Capital Asset Pricing Model’s (CAPM’s) Beta Estimation in the Presence of Normality and Non-normality Assumptions. Int J Finance Bank Res. 2017;3(3):44-52. doi: 10.11648/j.ijfbr.20170303.12

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  • @article{10.11648/j.ijfbr.20170303.12,
      author = {Ezekiel Oseni and Razak Olawale Olanrewaju},
      title = {A Capital Asset Pricing Model’s (CAPM’s) Beta Estimation in the Presence of Normality and Non-normality Assumptions},
      journal = {International Journal of Finance and Banking Research},
      volume = {3},
      number = {3},
      pages = {44-52},
      doi = {10.11648/j.ijfbr.20170303.12},
      url = {https://doi.org/10.11648/j.ijfbr.20170303.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijfbr.20170303.12},
      abstract = {This study describes the approach for estimating the beta-risk of the Capital Asset Price Model (CAPM) when the normality (Gaussian) assumption of both the error term and the excess return on an asset holds, and also when their normality assumption is violated or failed due to outliers or excessive skewness and excessive kurtosis. The student-t distribution was used as an alternative distribution to capture these anomalies. The monthly All-share Index (ASI) of 12 crucial Market Portfolios / Sectors derived from Nigeria Stock Exchange (NSE) were subjected to both the Gaussian error innovation and Student-t error innovation in this study. However, it was noted that estimates of portfolios’ beta-risk and its standard error for Gaussian and student-t were approximately the same when the sector follows a normal distribution while the standard errors of portfolio beta-risk estimates will be smaller under student-t innovation than that of Gaussian innovation when the sector does not follow normal distribution due to these anomalies. Furthermore, it was discovered that building & construction, manufacturing, quarry & mining, communication, transportation, education and utilities sectors have been having lower volatility, that is, in boosting the economy over the last 15 years.},
     year = {2017}
    }
    

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    AU  - Ezekiel Oseni
    AU  - Razak Olawale Olanrewaju
    Y1  - 2017/06/16
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    N1  - https://doi.org/10.11648/j.ijfbr.20170303.12
    DO  - 10.11648/j.ijfbr.20170303.12
    T2  - International Journal of Finance and Banking Research
    JF  - International Journal of Finance and Banking Research
    JO  - International Journal of Finance and Banking Research
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    PB  - Science Publishing Group
    SN  - 2472-2278
    UR  - https://doi.org/10.11648/j.ijfbr.20170303.12
    AB  - This study describes the approach for estimating the beta-risk of the Capital Asset Price Model (CAPM) when the normality (Gaussian) assumption of both the error term and the excess return on an asset holds, and also when their normality assumption is violated or failed due to outliers or excessive skewness and excessive kurtosis. The student-t distribution was used as an alternative distribution to capture these anomalies. The monthly All-share Index (ASI) of 12 crucial Market Portfolios / Sectors derived from Nigeria Stock Exchange (NSE) were subjected to both the Gaussian error innovation and Student-t error innovation in this study. However, it was noted that estimates of portfolios’ beta-risk and its standard error for Gaussian and student-t were approximately the same when the sector follows a normal distribution while the standard errors of portfolio beta-risk estimates will be smaller under student-t innovation than that of Gaussian innovation when the sector does not follow normal distribution due to these anomalies. Furthermore, it was discovered that building & construction, manufacturing, quarry & mining, communication, transportation, education and utilities sectors have been having lower volatility, that is, in boosting the economy over the last 15 years.
    VL  - 3
    IS  - 3
    ER  - 

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Author Information
  • Department of Banking and Finance, Faculty of Business Administration, University of Lagos, Lagos, Nigeria

  • Department of Statistics, Faculty of Science, University of Ibadan, Ibadan, Nigeria

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