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Stochastic Asset Models for Actuarial Use in Ghana

Received: 28 May 2017     Accepted: 12 June 2017     Published: 16 January 2018
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Abstract

The need for stochastic asset models has evolved from a common global standard for risk management in the Solvency II regime in Europe, IAIS Common Principles, Global ORSA standards NAIC, EIOPA, and OSFI. But the challenges in developing markets such as; lack of good quality data, inconsistent data coverage, market data not having long enough history, and lack of liquidity in certain parts of asset market have caused the absence of such models in Ghana. There have been a number of actuarial stochastic asset models designed for simulating future economic and investment conditions in several parts of the world. This study has discussed three of such models and determined which best fits the Ghanaian economic data. The data used for the empirical analysis in this study were taken from the Bank of Ghana database and the Ghana Stock Exchange. The study re-calibrated the models to derive the parameter set then compared the model results numerically after running 10000 simulations for 50 horizons. Investigations about the basic statistics of the simulated results for all the models are compared. The analysis revealed that all of the Ghanaian investment series used in the stochastic investment modeling are non-stationary in their mean, variance and auto-covariance. The study then found that the “Wilkie linear model” produced simulated values with similar characteristics to the historical data whiles the Whitten & Thomas TAR model produced simulated values with minimal forecast error. The study therefore suggests that since the “Wilkie linear model” has a relatively better parsimony, ready economic interpretation and its ability to mimic some important features of the Ghanaian economic series it deserves the attention of the actuary seeking to model jointly the behavior of asset returns and economic variables that matter in economic capital determination of insurance and pension business in Ghana.

Published in International Journal of Statistical Distributions and Applications (Volume 3, Issue 4)
DOI 10.11648/j.ijsd.20170304.20
Page(s) 129-139
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), 2018. Published by Science Publishing Group

Keywords

Wilkie Linear Model, TAR Model, Stochastic, Asset, Simulations, Ghana

References
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  • APA Style

    Evans Tee, Eric Dei Ofosu-hene. (2018). Stochastic Asset Models for Actuarial Use in Ghana. International Journal of Statistical Distributions and Applications, 3(4), 129-139. https://doi.org/10.11648/j.ijsd.20170304.20

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    ACS Style

    Evans Tee; Eric Dei Ofosu-hene. Stochastic Asset Models for Actuarial Use in Ghana. Int. J. Stat. Distrib. Appl. 2018, 3(4), 129-139. doi: 10.11648/j.ijsd.20170304.20

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    AMA Style

    Evans Tee, Eric Dei Ofosu-hene. Stochastic Asset Models for Actuarial Use in Ghana. Int J Stat Distrib Appl. 2018;3(4):129-139. doi: 10.11648/j.ijsd.20170304.20

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  • @article{10.11648/j.ijsd.20170304.20,
      author = {Evans Tee and Eric Dei Ofosu-hene},
      title = {Stochastic Asset Models for Actuarial Use in Ghana},
      journal = {International Journal of Statistical Distributions and Applications},
      volume = {3},
      number = {4},
      pages = {129-139},
      doi = {10.11648/j.ijsd.20170304.20},
      url = {https://doi.org/10.11648/j.ijsd.20170304.20},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20170304.20},
      abstract = {The need for stochastic asset models has evolved from a common global standard for risk management in the Solvency II regime in Europe, IAIS Common Principles, Global ORSA standards NAIC, EIOPA, and OSFI. But the challenges in developing markets such as; lack of good quality data, inconsistent data coverage, market data not having long enough history, and lack of liquidity in certain parts of asset market have caused the absence of such models in Ghana. There have been a number of actuarial stochastic asset models designed for simulating future economic and investment conditions in several parts of the world. This study has discussed three of such models and determined which best fits the Ghanaian economic data. The data used for the empirical analysis in this study were taken from the Bank of Ghana database and the Ghana Stock Exchange. The study re-calibrated the models to derive the parameter set then compared the model results numerically after running 10000 simulations for 50 horizons. Investigations about the basic statistics of the simulated results for all the models are compared. The analysis revealed that all of the Ghanaian investment series used in the stochastic investment modeling are non-stationary in their mean, variance and auto-covariance. The study then found that the “Wilkie linear model” produced simulated values with similar characteristics to the historical data whiles the Whitten & Thomas TAR model produced simulated values with minimal forecast error. The study therefore suggests that since the “Wilkie linear model” has a relatively better parsimony, ready economic interpretation and its ability to mimic some important features of the Ghanaian economic series it deserves the attention of the actuary seeking to model jointly the behavior of asset returns and economic variables that matter in economic capital determination of insurance and pension business in Ghana.},
     year = {2018}
    }
    

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    AU  - Evans Tee
    AU  - Eric Dei Ofosu-hene
    Y1  - 2018/01/16
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    JF  - International Journal of Statistical Distributions and Applications
    JO  - International Journal of Statistical Distributions and Applications
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    PB  - Science Publishing Group
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    AB  - The need for stochastic asset models has evolved from a common global standard for risk management in the Solvency II regime in Europe, IAIS Common Principles, Global ORSA standards NAIC, EIOPA, and OSFI. But the challenges in developing markets such as; lack of good quality data, inconsistent data coverage, market data not having long enough history, and lack of liquidity in certain parts of asset market have caused the absence of such models in Ghana. There have been a number of actuarial stochastic asset models designed for simulating future economic and investment conditions in several parts of the world. This study has discussed three of such models and determined which best fits the Ghanaian economic data. The data used for the empirical analysis in this study were taken from the Bank of Ghana database and the Ghana Stock Exchange. The study re-calibrated the models to derive the parameter set then compared the model results numerically after running 10000 simulations for 50 horizons. Investigations about the basic statistics of the simulated results for all the models are compared. The analysis revealed that all of the Ghanaian investment series used in the stochastic investment modeling are non-stationary in their mean, variance and auto-covariance. The study then found that the “Wilkie linear model” produced simulated values with similar characteristics to the historical data whiles the Whitten & Thomas TAR model produced simulated values with minimal forecast error. The study therefore suggests that since the “Wilkie linear model” has a relatively better parsimony, ready economic interpretation and its ability to mimic some important features of the Ghanaian economic series it deserves the attention of the actuary seeking to model jointly the behavior of asset returns and economic variables that matter in economic capital determination of insurance and pension business in Ghana.
    VL  - 3
    IS  - 4
    ER  - 

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Author Information
  • Department of Business Administration, Regentropfen College of Applied Sciences, Bolgatanga, Ghana

  • Department of Finance, University of Ghana Business School, University of Ghana, Legon, Ghana

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