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 |
Beta-Risk, Capm, Expected Returns, Gaussian Innovation, Student-T Innovation, Systematic Risk
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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
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
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
@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} }
TY - JOUR T1 - A Capital Asset Pricing Model’s (CAPM’s) Beta Estimation in the Presence of Normality and Non-normality Assumptions AU - Ezekiel Oseni AU - Razak Olawale Olanrewaju Y1 - 2017/06/16 PY - 2017 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 SP - 44 EP - 52 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 -